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Lawrence Berkeley National Laboratory

Title:

Research Project on CO2 Geological Storage and Groundwater Resources: Water Quality Effects

Caused by CO2 Intrusion into Shallow Groundwater

Author:

Birkholzer, Jens

Publication Date:

12-18-2008

Publication Info:

Lawrence Berkeley National Laboratory

Permalink:

http://escholarship.org/uc/item/82c0q660
 

Research Project on CO2 Geological Storage and 

Groundwater Resources 

Water Quality Effects Caused by CO2 Intrusion into 

Shallow Groundwater 

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Technical Report, October 2008 

Contact: Jens Birkholzer 

Earth Sciences Division, Lawrence Berkeley National Laboratory 

1 Cyclotron Rd., MS 90-1116; 510-486-7134; jtbirkholzer@lbl.gov

Contributors: 

Jens Birkholzer, John Apps, Liange Zheng, Yingqi Zhang, Tianfu Xu, Chin-Fu Tsang 

EPA Program Managers: Bruce Kobelski, Anhar Karimjee 
 

i 

Executive Summary 

One promising approach to reduce greenhouse gas emissions is injecting CO2 into suitable 

geologic formations, typically depleted oil/gas reservoirs or saline formations at depth larger 

than 800 m. Proper site selection and management of CO2 storage projects will ensure that the 

risks to human health and the environment are low. However, a risk remains that CO2 could 

migrate from a deep storage formation, e.g. via local high-permeability pathways such as 

permeable faults or degraded wells, and arrive in shallow groundwater resources. The ingress of 

CO2 is by itself not typically a concern to the water quality of an underground source of drinking 

water (USDW), but it will change the geochemical conditions in the aquifer and will cause 

secondary effects mainly induced by changes in pH, in particular the mobilization of hazardous 

inorganic constituents present in the aquifer minerals. Identification and assessment of these 

potential effects is necessary to analyze risks associated with geologic sequestration of CO2. 

This report describes a systematic evaluation of the possible water quality changes in response to 

CO2 intrusion into aquifers currently used as sources of potable water in the United States. Our 

goal was to develop a general understanding of the potential vulnerability of United States 

potable groundwater resources in the event of CO2 leakage. This goal was achieved in two main 

tasks, the first to develop a comprehensive geochemical model representing typical conditions in 

many freshwater aquifers (Section 3), the second to conduct a systematic reactive-transport 

modeling study to quantify the effect of CO2 intrusion into shallow aquifers (Section 4). Via 

reactive-transport modeling, the amount of hazardous constituents potentially mobilized by the 

ingress of CO2 was determined, the fate and migration of these constituents in the groundwater 

was predicted, and the likelihood that drinking water standards might be exceeded was 

evaluated. A variety of scenarios and aquifer conditions was considered in a sensitivity 

evaluation. The scenarios and conditions simulated in Section 4, in particular those describing 

the geochemistry and mineralogy of potable aquifers, were selected based on the comprehensive 

geochemical model developed in Section 3. 

An important step in developing the comprehensive geochemical model was the identification of 

those mineral hosts containing hazardous constituents, which control their equilibrium 

concentration in potable groundwaters. This step was accomplished through an extensive review 

of the literature, especially that relating to ore and coal mineralogy, through which plausible 

mineral hosts containing hazardous constituents, either as essential components or in solid 

solution, were provisionally identified. The postulated presence of these mineral hosts in aquifer 

rocks was then examined through a thermodynamic analysis of more than 38,000 potable 

groundwaters from the USGS NWIS database, using a distribution-of-species code to test for 

their thermodynamic saturation in solution. Mineral hosts meeting the criterion of 

thermodynamic saturation were assumed to control the aqueous concentrations of hazardous 

constituents and any subsequent changes due to the ingress of CO2. Considering a representative 

groundwater under reducing conditions (a condition met in the large majority of all evaluated 

groundwaters), the most likely solubility-controlling mineral host for lead is galena, while 

arsenic would typically be controlled by the arsenopyrite component as a solid solution in pyrite. 

Other likely mineral hosts for a reducing groundwater are as follows: barium is likely to be 
 

ii 

saturated with respect to barite; cadmium is either in solid solution in sphalerite and/or is present 

as greenockite or cadmoselite; mercury is likely to be present as cinnabar and possibly as 

tiemannite, but its presence in solid solution in pyrite, perhaps in minor amounts, cannot be ruled 

out; antimony is present as the gudmundite component in solid solution in pyrite, and probably 

as kermesite, especially in neutral to acid conditions; selenium could be present as either 

clausthalite, tiemannite, or cadmoselite; uranium is present as uraninite and/or coffinite; and zinc 

is present as sphalerite and/or hemimorphite. 

Characterization of thermodynamic controls necessitated the iterative improvement and 

augmentation of a large supporting thermodynamic database comprising the solubility products 

of minerals of interest, and of complexes that affect the concentrations of the hazardous 

constituents in aqueous solution. A comprehensive literature analysis was conducted for this 

purpose. The resulting thermodynamic data used for geochemical modeling were then tested, and 

in some instances improved via calibration against field observations from the NWIS database, 

based on the resulting thermodynamic saturations of the more than 38,000 groundwater samples. 

In addition, the bulk mineralogy of typical aquifer host rocks had also to be determined, 

accomplished through review of the scientific literature. The bulk mineralogy was combined 

with the mineralogy of the previously identified mineral hosts for several hazardous inorganic 

constituents, with their respective concentrations estimated through an evaluation of natural 

abundances in soils and sediments. While these abundances are typically very small compared to 

those of the major minerals (order of 10-7 to >10-4), all hazardous constituents targeted for study 

occur ubiquitously in soils, sediments, and aquifer rocks, indicating that their potential 

mobilization in response to CO2 intrusion can be a widespread one in domestic aquifers. 

A preliminary assessment of CO2-related water quality changes was conducted by calculating the 

expected equilibrium concentrations of hazardous inorganic constituents as a function of the 

partial pressure of CO2. P(CO2) was varied from 10-4 bar to 10 bar, leading to a corresponding 

pH variation between 8.9 and 5.6. Equilibrium concentrations were determined using a 

distribution-of-species code, assuming a representative groundwater in reducing conditions and 

incorporating the thermodynamic controls identified in above-mentioned database evaluation. 

Results indicate that the concentrations of some of the studied constituents, for example barium, 

cadmium, antimony, and zinc, are sensitive to changes in P(CO2), but that only arsenic, and to a 

lesser extent lead and zinc, have the potential for exceeding their respective maximum 

contaminant levels (as defined by the U.S. Environmental Protection Agency) at elevated CO2

concentrations. Therefore, in the systematic reactive-transport modeling study discussed below, 

primary emphasis was placed on the evaluation of the response and transport of arsenic and lead 

under intruding CO2. 

The reactive-transport simulator TOUGHREACT was then used to predict the transport of 

gaseous and dissolved CO2 within a shallow aquifer, as well as the geochemical changes caused 

by the presence of CO2, in particular those changes leading to the mobilization of lead and 

arsenic and their migration in the groundwater (Section 4). Various sensitivity studies were 

conducted for different hydrological, geochemical, and mineralogical conditions, (1) to account 

for a range of conditions found in domestic potable aquifers and their host rocks, and (2) to 
 

iii 

evaluate the uncertainty associated with geochemical processes and model parameters. 

Equilibrium runs were performed prior to modeling CO2 intrusion to establish the initial 

chemical composition of the groundwater at equilibrium with the selected mineralogy. This 

initiation step also establishes equilibrium between mineral solubilities and adsorption sites, 

essentially “loading up” the latter. Thus, the selection of solubility-controlling minerals for 

hazardous inorganic constituents not only defined the initial aqueous concentration in the 

groundwater, but also the amount of contaminants initially sorbed onto clays and iron oxides. 

Starting with the equilibrated initial conditions, the impact of CO2 intrusion was then modeled 

for a synthetic hydrogeological situation in a representative potable aquifer while considering 

continuous ingress of CO2 for a time period of 100 years. CO2 was assumed to have escaped 

from a deep geologic sequestration site via a preferential pathway, entering the shallow aquifer 

locally at a constant rate. A confined aquifer was considered, meaning that excess CO2 not 

dissolving into groundwater accumulates at the aquifer top and spreads out laterally, potentially 

causing widespread pH changes and mobilization of contaminants. (Excess CO2 in an 

unconfined aquifer would escape into the vadose zone, causing no further harm to the 

groundwater quality.) A suite of “representative” aquifer mineralogies was simulated 

representing a broad range of host rock compositions found throughout the United States. 

Our simulation results suggest that CO2 ingress into a shallow aquifer can lead to significant 

mobilization of lead and arsenic, contaminating the groundwater near the intrusion location and 

further downstream. While substantial increases in aqueous concentrations are predicted to occur 

compared to the initial water composition, the maximum contaminant level for arsenic in 

groundwater has been exceeded in only a few simulation cases, whereas the maximum 

contaminant level for lead has not been exceeded at all. Notice that the mobilization of lead and 

arsenic, from minerals and from adsorption sites, causes only small changes to the initial 

contaminant mass in the solid phase, even after a time period of 100 years of CO2 intrusion. Very 

small amounts of lead and arsenic contained in the aquifer solid phase can provide a long-lasting 

source of contamination. 

As demonstrated in a comprehensive sensitivity study, many conditions and processes impact the 

severity and spatial distribution of groundwater contamination in response to CO2 intrusion. Our 

simulations indicate that adsorption/desorption via surface complexation is arguably the most 

important process controlling the fate of hazardous constituents mobilized by CO2 leakage when 

sorbing minerals are present. In a simulation case without adsorption/desorption taken into 

account, the resulting long-term contamination of the aquifer is less significant near the region 

where CO2 enters, while aqueous concentrations keep increasing with travel distance as acidic 

water migrates further and more dissolution occurs. The relative importance of dissolution/precipitation versus adsorption/desorption is controlled by many factors, including 

adsorption parameters and aquifer mineralogy, reaction kinetics, aqueous complexation 

processes, and mineral solubility constants. For example, the role that adsorption/desorption 

plays in the mobilization of hazardous constituents depends strongly on the number of adsorption 

sites. A sensitivity run with lower site density reveals that early increases in aqueous lead and 

arsenic are much less significant, because desorption is less relevant in mobilizing contaminants. 
 

iv 

Moderate to strong sensitivities in the resulting lead and arsenic concentrations have been 

obtained with respect to other geochemical parameters (such as the solubilities of hazardousconstituent-bearing minerals, the kinetic rates for precipitation and dissolution, and the aqueous 

complexation constants) and conditions (such as the redox environment, with the simulated 

behavior in a oxidized aquifer being very different from an aquifer in a reducing environment). 

Less relevant appears to be the initial volume fraction of trace- element minerals that contain 

lead and arsenic as essential components. As long as these trace minerals have not been 

completely dissolved (a very slow process), the contamination of an aquifer via dissolution 

and/or desorption is fairly independent of the initial mineral volume. With respect to the 

representative aquifer mineralogies assumed in our study (ranging from almost pure arenites to 

dolomitic limestone), the most important effects resulted from differences in clay content, which 

affect the adsorption/desorption behavior, as well as from differences in the presence of 

carbonate minerals, which can moderate the pH changes caused by CO2 intrusion. 

Sensitivity studies have also been conducted with respect to the assumed flow and CO2 leakage 

conditions in the aquifer. It turns out that an increase in the leakage rate does not necessarily lead 

to increases in the maximum contaminant concentrations in the aquifer. Once the groundwater 

reaches its solubility limit with respect to CO2, which occurs at relatively small intrusion rates, 

there is no further increase in total inorganic carbon and acidity. Consequently, no further 

mobilization of hazardous constituents occurs once local equilibrium has been achieved. Excess 

CO2 remains in the gas phase and rises to the top of the aquifer, thereby bypassing most of the 

groundwater body. The resulting impact on aqueous contamination is influenced by the general 

pattern of the CO2 plume (e.g., the spreading of the plume at the top of the aquifer in the case of 

a confined aquifer) and the subsequent migration of CO2-charged and contaminated water further 

downstream. Similar effects on the dissolution characteristics can be seen when the groundwater 

flow rate changes, as more or less groundwater undersaturated with respect to CO2 will flow past 

the location where CO2 enters the aquifer. 

As stated before, the goal of this study was to better understand, in a global sense, the 

vulnerability of representative and relevant aquifer types in the case of CO2 intrusion, thereby 

providing sound scientific information to regulators and the public. It is understood, however, 

that even the most comprehensive sensitivity study cannot encompass all possible aquifer 

conditions and CO2 intrusion scenarios. Neither can it account for all relevant uncertainties. 

While the geochemical evaluation conducted in Section 3 was extremely comprehensive, it was 

also pointed out that several assumptions, approximations, and compromises were necessary in 

this effort, mostly because of incomplete characterization of important parameters such as 

dissolution and dissociation constants. We recommend that our study should now be followed up 

with site-specific model predictions, and that laboratory or field experiments should be 

performed to test uncertain model assumptions and parameters. Such experiments would involve 

exposing initially undisturbed groundwater in laboratory samples or in situ in the field to 

elevated levels of CO2. We also recommend testing the developed geochemical models using 

measured data from natural analogue sites, such as those described in Section 5 of this report. 
 

v 

Table of Contents 

1. Motivation.......................................................................................................................... 1

2. Work Scope and Research Plan....................................................................................... 4

3. Geochemical Model Definition......................................................................................... 9

3.1 Basic Methodology......................................................................................................... 9

3.2 Identification of Mineral Hosts Controlling the Concentration of Hazardous 

Constituents .................................................................................................................. 15 

3.3 Evaluation of Potable Groundwater Quality in Relation to Groundwater Model 

Development................................................................................................................. 38 

3.3.1 Background ............................................................................................................... 38

3.3.2 Concentration of Major Constituents in Potable Groundwater ............................... 40

3.3.3 Concentrations of Hazardous Inorganic Constituents in Potable 

Groundwater ............................................................................................................. 46

3.3.4 Detection Limits of Hazardous Inorganic Constituents in Groundwater................. 55

3.3.5 Analytical Artifacts Affecting Measurements of Hazardous Inorganic 

Constituents in Groundwater.................................................................................... 57

3.4 Thermodynamic Analysis of Potable Groundwater...................................................... 62 

3.4.1 Calculation of Activities and Saturation Indices ...................................................... 63

3.4.2 Modification and Augmentation of the Thermodynamic Data Supporting the 

Codes EQ3/6 and TOUGHREACT ........................................................................... 64

3.4.3 Evaluating the Integrity of Water Quality Analyses ................................................. 69

3.4.4 Evaluation of Groundwater Redox State .................................................................. 84

3.4.5 Evaluation of Solubility Controls for Hazardous Constituents ................................ 88

3.4.5.1 Discussion of Results from Run 0..............................................................................................91

3.4.5.2 Discussion of Results from Run 1............................................................................................112

3.4.6 Variation of Lead, Cadmium, and Zinc Concentration with Redox Potential........ 123

3.4.7 Summary of Thermodynamic Controls ................................................................... 134
 

vi 

3.5 The Variation of Hazardous Constituent Concentrations with Partial Pressure of 

Carbon Dioxide in Groundwaters............................................................................... 137 

3.5.1 Introduction............................................................................................................. 137

3.5.2 Input Specifications................................................................................................. 137

3.5.3 Results from Equilibrium Calculations................................................................... 139

3.5.4 Uncertainties Associated with Equilibrium Calculations....................................... 144

3.5.4.1 Estimation of Aggregate Uncertainties ....................................................................................144

3.5.4.2 Discussion Relating to Uncertainties........................................................................................156

3.5.5 System Variations with P(CO2) and the Impact of Complexation .......................... 158

3.5.5.1 Variation of pH, Eh and P(H2S) with P(CO2) ..........................................................................158

3.5.5.2 Nature of Aqueous Complexation and its Impact on Mineral Solubility .................................159

3.5.6 Conclusions from Equilibrium Calculations .......................................................... 167

3.6 Representative Aquifer Host Rock Mineralogies ....................................................... 169 

3.6.1 Background ............................................................................................................. 169

3.6.2 Approach at Mineralogy Characterization............................................................. 171

3.6.2.1 Distribution of Major and Accessory Minerals ........................................................................171

3.6.2.2 Distribution of Trace-Element Minerals ..................................................................................176

3.6.3 Representative Mineralogies .................................................................................. 179

3.6.3.1 Coastal Plain Aquifer Host Rock .............................................................................................179

3.6.3.2 Coastal Plain Aquifer Host Rock - Oxidizing Conditions........................................................180

3.6.3.3 St Peter Sandstone....................................................................................................................181

3.6.3.4 Dolomitic Limestone................................................................................................................181

3.7 Summary Results and Implications for Reactive Transport Model of CO2

Intrusion...................................................................................................................... 184 

4. Systematic Geochemical Modeling.............................................................................. 195

4.1 TOUGHREACT Simulator......................................................................................... 195 

4.1.1 Thermodynamic Data and Kinetic Data ................................................................. 197

4.1.2 Improved Adsorption Models.................................................................................. 198
 

vii 

4.2 Model Setup................................................................................................................ 199 

4.3 Model Sequence and Sensitivity Cases ...................................................................... 203 

4.4 Initial Geochemical Conditions for Base Model with Coastal Plain Sandstone......... 205 

4.5 Results for CO2 Intrusion into Coastal Plain Sandstone............................................. 215 

4.5.1 Prediction of CO2 Plume Evolution and Changes in Acidity ................................. 215

4.5.2 Prediction of Pb and As Concentrations ................................................................ 219

4.6 Sensitivity Analyses.................................................................................................... 229

4.6.1 Sensitivity to Adsorption ......................................................................................... 229

4.6.1.1 Sensitivity Run without Adsorption .........................................................................................229

4.6.1.2 Sensitivity Run with Lower Site Density .................................................................................233

4.6.1.3 Sensitivity to Surface Complexation Constant.........................................................................237

4.6.2 Sensitivity to Kinetic Rate Parameter ..................................................................... 239

4.6.2.1 Sensitivity to Kinetic Rate Parameter for Galena.....................................................................239

4.6.2.2 Sensitivity to Kinetic Rate Parameter for Arsenopyrite ...........................................................241

4.6.3 Sensitivity to PbS(aq) Dissociation Constant ......................................................... 243

4.6.4 Sensitivity to Mineral Solubility.............................................................................. 245

4.6.4.1 Sensitivity to Solubility of Galena ...........................................................................................245

4.6.4.2 Sensitivity to Solubility of Arsenopyrite..................................................................................247

4.6.5 Sensitivity to Initial Mineral Volume Fraction ....................................................... 248

4.6.5.1 Sensitivity to Initial Volume Fraction of Galena......................................................................248

4.6.5.2 Sensitivity to Initial Volume Fraction of Arsenopyrite ............................................................249

4.6.6 Sensitivity to Initial Partial Pressure of CO2.......................................................... 250

4.6.7 Sensitivity to Initial Fluid Pressure (Depth of Aquifer).......................................... 254

4.6.8 Sensitivity to Groundwater Flow Velocity .............................................................. 256

4.6.9 Sensitivity to CO2 Leakage Rate ............................................................................. 259

4.7 Results for Other Mineralogies................................................................................... 263 

4.7.1 St Peter Sandstone .................................................................................................. 263
 

viii 

4.7.2 Dolomitic Limestone ............................................................................................... 271

4.8 Oxidizing Conditions.................................................................................................. 277 

4.9 Three-Dimensional (3D) Simulation of CO2 Intrusion............................................... 282 

4.9.1 Results from 3D Simulation of Base Model ............................................................ 283

4.9.2 Sensitivity to Groundwater Flow Velocity—3D Model .......................................... 293

4.9.3 Sensitivity to CO2 Leakage Rate — 3D Model........................................................ 302

4.10 Summary of Modeling Results ................................................................................... 311 

5. Analogues....................................................................................................................... 320

5.1 Natural Analogues ...................................................................................................... 320 

5.1.1 San Vittorino, Italy.................................................................................................. 320

5.1.2 Florina Basin, Greece............................................................................................. 321

5.1.3 Albani Hills, Italy.................................................................................................... 322

5.1.4 Central Italy ............................................................................................................ 323

5.1.5 Mammoth Mountain, California, USA.................................................................... 324

5.1.6 Paradox Basin, Utah, USA ..................................................................................... 325

5.2 Landfill Gas Impacts................................................................................................... 325 

6. Overall Conclusions and Recommendations.............................................................. 326

7. Acknowledgments ......................................................................................................... 330

8. References...................................................................................................................... 330

Appendices A through F 
 

ix 

List of Figures 

Fig. 1.1. Schematic illustrating potential groundwater quality changes in response to CO2 leakage 

from deep storage sites.........................................................................................................................1

Fig. 2.1. Main research steps and information flow between them....................................................................6 

Fig. 3.1. Flow chart describing research steps providing necessary information as input to reactive 

transport models.................................................................................................................................14 

Fig. 3.2. Concentrations (per gram of sediment) of selected trace elements and Fe in the pyritic 

and reactive fractions of organic-rich marsh sediments from Atchafalaya Bay with depth 

below the sediment-water interface (after Huerta-Diaz and Morse, 1992). Dashed lines 

are illustrative only, and included to emphasize trends in the partitioning of trace metals 

between the reactive and pyritic components. ...................................................................................23 

Fig. 3.3. The degree of trace metal pyritization (DTMP) in terms of the degree of pyritization 

(DOP) for As, Cd, Hg, Pb and Zn in organic-rich marsh sediments from Atchafalaya Bay 

(after Huerta-Diaz and Morse, 1992).................................................................................................24 

Fig. 3.4. The correlation between the molar concentration of Se and As, determined by laserablation ICP-MS, in pyrite from Ohio 5/6/7 bituminous coal (from Kolker et al., 2002)..................33 

Fig. 3.5. Histograms showing the concentration distributions of various chemical constituents, as 

well as pH and calculated Eh in more than 38,000 analyses of potable groundwaters in 

the United States. ...............................................................................................................................44 

Fig. 3.6. Histograms showing the concentration distributions of selected hazardous constituents in 

more than 38,000 analyses of potable groundwaters in the United States. MCL signifies 

the maximum contaminant level for each constituent. SDWR stands for secondary 

drinking water regulation...................................................................................................................48 

Fig. 3.7. Spatial distribution of hazardous constituents concentrations in soils and surficial 

sediments of (a) Pb and (b) As (c) Ba and (d) U in the forty-eight contiguous states, from 

National Geochemical Survey Database (USGS, 2008). ...................................................................52 

Fig. 3.8. Spatial distribution of samples with elevated aqueous concentration (Log mg/L) of (a) Pb, 

(b) As (c) Ba and (d) U in the forty-eight contiguous states. The plotted concentrations 

were drawn from more than 38,000 groundwater quality analyses downloaded from the 

National Water Information System Database (NWIS).....................................................................55 

Fig. 3.9. Frequency distribution of Pb analyses in groundwaters using the analytical method 

PLM43 (ICP-MS). For reference, the predicted equilibrium concentration for Pb+2 is 

0.0001 mg/L (or -4 in log mg/L), calculated assuming that galena co-exists with pyrite 

and goethite in a typical reducing groundwater. ................................................................................60 

Fig. 3.10. Frequency distribution of As analyses in groundwaters using the analytical methods 

PLM10 and PLM40 (ICP-MS). .........................................................................................................62 

Fig. 3.11. Histogram showing the pH distribution of more than 35,000 groundwater analyses 

selected from the NWIS Database. No correction for charge imbalance...........................................74 

Fig. 3.12. Histogram showing the distribution of percentage charge imbalance of more than 35,000 

groundwater analyses selected from the NWIS Database..................................................................74 
 

x 

Fig. 3.13. Histogram showing the calculated calcite saturation indices of more than 35,000 

groundwater analyses selected from the NWIS Database. No correction for charge 

imbalance...........................................................................................................................................75 

Fig. 3.14. Histogram showing the calculated dolomite saturation indices of more than 35,000 

groundwater analyses selected from the NWIS Database. No correction for charge 

imbalance...........................................................................................................................................76 

Fig. 3.15. Histogram showing the calculated gypsum saturation indices of more than 35,000 

groundwater analyses selected from the NWIS Database. No correction for charge 

imbalance...........................................................................................................................................77 

Fig. 3.16. Histogram showing the distribution of calculated siderite saturation indices of more than 

35,000 groundwater analyses selected from the NWIS Database. No correction for charge 

imbalance...........................................................................................................................................78 

Fig. 3.17. Histogram showing the distribution of calculated Log P(CO2) of more than 35,000 

groundwater analyses selected from the NWIS Database. No correction for charge 

imbalance...........................................................................................................................................79 

Fig. 3.18. Histogram showing the distribution of calculated Log P(H2S) distribution of more than 

35,000 groundwater analyses selected from the NWIS Database. No correction for 

charge imbalance. ..............................................................................................................................80 

Fig. 3.19. Histogram showing the distribution of calculated calcite saturation indices of more than 

35,000 groundwater analyses selected from the NWIS Database. Charge imbalance 

corrected through adjustment of HCO3

-

 concentration. .....................................................................81 

Fig. 3.20. Histogram showing the distribution of calculated siderite saturation indices of more than 

35,000 groundwater analyses selected from the NWIS Database. Charge imbalance 

corrected through adjustment of HCO3

-

 concentration. .....................................................................82 

Fig. 3.21. Histogram showing the distribution of calculated Log P(CO2) of more than 35,000 

groundwater analyses selected from the NWIS Database. Charge imbalance corrected 

through adjustment of HCO3

-

 concentration. .....................................................................................82 

Fig. 3.22. Calculated Eh, based on the distribution of Fe(III) and Fe(II), and the assumption that 

Fe(III) is in equilibrium with goethite, plotted against measured pH. ...............................................87 

Fig. 3.23. Frequency Distribution of Saturation Indices of (a) Pyrite, (b) Magnetite, (c) Siderite and 

(d) Chlorite. Saturation Indices are calculated assuming Eh to be defined by Fe(III)/Fe(II) 

in solution in the presence of goethite, using a selected set of grounwaters from the 

NWIS. ................................................................................................................................................93 

Fig. 3.24. Frequency Distribution of Saturation Indices of Clinochlore. Clinochlore saturation is not 

significantly affected by variation in Eh. ...........................................................................................94 

Fig. 3.25. Frequency Distribution of Saturation Indices of (a) Arsenopyrite, (b) Scorodite, and (c) 

Symplesite. The Saturation Indices are calculated using a selected set of groundwaters 

from the NWIS and assuming Eh to be defined by Fe(III)/Fe(II) in solution....................................95 

Fig. 3.26. Frequency Distribution of Saturation Indices of (a) Barite and (b) Witherite. The 

Saturation Indices are calculated for a selected set of groundwaters from the NWIS. Eh is 

defined by Fe(III)/Fe(II) in solution, which generally does not significantly affect either 

barite or witherite saturation. .............................................................................................................96 

Fig. 3.27. Frequency Distribution of Saturation Indices of (a) CdS (Greenockite), (b) Cadmoselite 

and (c) Otavite. The Saturation Indices are calculated using a selected set of 

groundwaters from the NWIS. Eh is defined by Fe(III)/Fe(II) in solution, which generally 

does not significantly affect otavite saturation...................................................................................98 
 

xi 

Fig. 3.28. Frequency Distribution of the Saturation Index of (a) Cinnabar, (b) Tiemannite, and (c) 

Quicksilver. The Saturation Indices are calculated using a selected set of groundwaters 

from the NWIS and assuming Eh to be defined by Fe(III)/Fe(II) in solution..................................100 

Fig. 3.29. Frequency Distribution of Saturation Indices of (a) Galena, (b) Clausthalite, (c) Cerussite, 

(d) Hydrocerussite (e) Plumbonacrite (f) Anglesite and (g) Mimetite in a selected set of 

groundwaters from the NWIS. The Saturation Indices are calculated assuming Eh to be 

defined by Fe(III)/Fe(II) in solution. Cerussite and anglesite saturations are not 

significantly affected by variations in Eh.........................................................................................103 

Fig. 3.30. Frequency Distribution of Saturation Indices of (a) Gudmundite, (b) Berthierite, (c) 

Stibnite, (d) Antimonselite and (e) Kermesite. The Saturation Indices are calculated 

using a selected set of groundwaters from the NWIS, and assuming Eh to be defined by 

Fe(III)/Fe(II) in solution. .................................................................................................................106 

Fig. 3.31. Frequency Distribution of Saturation Indices of (a) Ferroselite, (b) Cadmoselite, (c) 

Tiemannite, (d) Clausthalite, and (e) Antimonselite. The Saturation Indices are 

calculated using a selected set of groundwaters from the NWIS, and assuming Eh to be 

defined by Fe(III)/Fe(II) in solution.................................................................................................109 

Fig. 3.32. Frequency Distribution of the Saturation Index of (a) Uraninite, (b) Coffinite and (c) 

Uranophane. The Saturation Indices are calculated using a selected set of groundwaters 

from the NWIS, and assuming Eh to be defined by Fe(III)/Fe(II) in solution.................................110 

Fig. 3.33. Frequency Distribution of the Saturation Indices of (a) Sphalerite, (b) Smithsonite, (c) 

Hydrozincite and (d) Hemimorphite. The Saturation Indices are calculated , using a 

selected set of groundwaters from the NWIS, and assuming Eh to be defined by 

Fe(III)/Fe(II) in solution. .................................................................................................................111 

Fig. 3.34. Frequency Distribution of Saturation Indices of (a) Arsenopyrite, (b) Scorodite, and (c) 

Symplesite. The Saturation Indices are calculated using a selected set of groundwaters 

from the NWIS, and assuming Eh to be defined by the coexistence of pyrite and goethite. ...........113 

Fig. 3.35. Frequency Distribution of the Saturation Index of Barite. The Saturation Indices are 

calculated for a selected set of groundwaters from the NWIS. Eh is defined by the 

coexistence of pyrite and goethite, but does not significantly affect barite saturation.....................114 

Fig. 3.36. Frequency Distribution of Saturation Indices of (a) CdS (Greenockite) and (b) 

Cadmoselite. The Saturation Indices are calculated assuming Eh to be defined by the 

coexistence of pyrite and goethite, using a selected set of groundwaters from the NWIS...............115 

Fig. 3.37. Frequency Distribution of the Saturation Index of (a) Cinnabar, (b) Tiemannite and (c) 

Quicksilver. The Saturation Indices are calculated assuming Eh to be defined by the 

coexistence of pyrite and goethite, using a selected set of groundwaters from the NWIS...............116 

Fig. 3.38. Frequency Distribution of Saturation Indices of (a) Galena, (b) Clausthalite and (c) 

Cerussite. Eh is defined by the coexistence of pyrite and goethite, but does not 

significantly affect cerussite saturation............................................................................................117 

Fig. 3.39. Frequency Distribution of Saturation Indices of (a) Gudmundite, (b) Stibnite, (c) 

Antimonselite and (d) Kermesite. The Saturation Indices are calculated assuming Eh to 

be defined by the coexistence of pyrite and goethite, using a selected set of groundwaters 

from the NWIS.................................................................................................................................119 

Fig. 3.40. Frequency Distribution of Saturation Indices of (a) Ferroselite, (b) Cadmoselite, (c) 

Tiemannite, (d) Clausthalite and (e) Antimonselite. The Saturation Indices are calculated 

assuming Eh to be defined by the coexistence of pyrite and goethite, using a selected set 

of groundwaters from the NWIS......................................................................................................121 
 

xii 

Fig. 3.41. Frequency Distribution of the Saturation Index of Uraninite. The Saturation Indices are 

calculated assuming Eh to be defined by the coexistence of pyrite and goethite, using a 

selected set of groundwaters from the NWIS. .................................................................................122

Fig. 3.42. Frequency Distribution of the Saturation Index of (a) Sphalerite, and (b) Hemimorphite. 

The Saturation Indices are calculated assuming Eh to be defined by the coexistence of 

pyrite and goethite, using a selected set of groundwaters from the NWIS. .....................................122 

Fig. 3.43. Calculated Pb Concentration in Groundwater as a Function of Eh and saturated with 

respect to Galena and Cerussite in the Presence of Goethite and Pyrite. Cerussite = cer; 

galena = gal; goe = goethite; py = pyrite. ........................................................................................125 

Fig. 3.44. Frequency Distribution of Saturation Indices of Galena in Groundwater Samples 

analyzed for Pb using PLM43 and PLM48. (a) Eh is defined by Fe(III)/Fe(II) in solution 

in the presence of goethite. (b) Eh is defined by the coexistence of pyrite and goethite. ...............127 

Fig. 3.45. Frequency Distribution of Saturation Indices of Clausthalite in Groundwater Samples 

analyzed for Pb using PLM43 and PLM48. (a) Eh is defined by Fe(III)/Fe(II) in solution 

in the presence of goethite. (b) Eh is defined by the coexistence of pyrite and goethite. ...............128 

Fig. 3.46. Frequency Distribution of Saturation Indices of (a) Pyrite and (b) Calcite in Groundwater 

Samples analyzed for Pb using PLM43 and PLM48. The Saturation Indices are calculated 

assuming Eh to be defined by Fe(III)/Fe(II) in solution in the presence of goethite........................128 

Fig. 3.47. Calculated Cd Concentration in Groundwater as a Function of Eh and saturated with 

respect to CdS and Otavite in the Presence of either Pyrite or Goethite. CdS = CdS; goe = 

goethite; ota = otavite; py = pyrite...................................................................................................130 

Fig. 3.48. Frequency Distribution of Saturation Indices of CdS in Groundwater Samples analyzed 

for Cd using PLM43 and PLM47. (a) Eh is defined by Fe(III)/Fe(II) in solution in the 

presence of goethite. (b) Eh is defined by the coexistence of pyrite and goethite. .........................131 

Fig. 3.49. Frequency Distribution of Saturation Indices of Cadmoselite in Groundwater Samples 

analyzed for Cd using PLM43 and PLM47. (a) Eh is defined by Fe(III)/Fe(II) in solution 

in the presence of goethite. (b) Eh is defined by the coexistence of pyrite and goethite. ...............132 

Fig. 3.50. Calculated Zn Concentration in Groundwater as a function of Eh and Saturated with 

respect to either Sphalerite or Hemimorphite in the Presence of Goethite and Pyrite. goe 

= goethite; py = pyrite; sph = sphalerite; hemi = hemimorphite.....................................................134 

Fig. 3.51. Variation of trace metal concentrations in equilibrium with host minerals as a function of 

CO2 partial pressure taking into account both sulfide and selenide complexation, and with 

solubility products calculated using thermochemical data and adjusted to ensure 

consistency with hazardous constituent concentrations in NWIS waters (see Table 3.14). 

(SS) refers to solid solution. CV013, CV014, CV016, XV017, CV018, FL007, PH0S3, 

PLM10, PLM40, PLM43, PLM47 and PLM48 are analytical methods cited in the NWIS 

database. See Table A2 in Appendix A. ..........................................................................................143 

Fig. 3.52. Histogram showing the temperature (oC) distribution of more than 35,000 groundwater 

analyses selected from the NWIS Database.....................................................................................155 

Fig. 3.53. Classification of arenaceous sediments in terms of detrital framework grains (quartz, 

feldspars and lithic fragments) and a detrital (clay) interstitial matrix (after Tucker, 1991). 

The spot on the 7.5-wt% matrix triangle represents the base-case aquifer host rock 

introduced in Section 3.6.3.1. The diamond represents the St Peter Sandstone described in 

Section 3.6.3.3. For comparison, the star illustrates the Gulf Coast Lithic Arkose 

described in Xu et al. (2006)............................................................................................................173 

Fig. 3.54. Representation of the tectonic provenances of framework grains of arenaceous rocks 

(after Tucker, 1991). TE = trailing edge or passive continental margin; SS = strike slip 

continental margin; CA = converging arc margin; BA = back arc basin and FA = fore arc 
 

xiii 

basin. The black spot indicates the base-case aquifer host rock introduced in Section 

3.6.3.1. The diamond represents the St Peter Sandstone described in Section 3.6.3.3. For 

comparison, the star illustrates the Gulf Coast Lithic Arkose described in Xu et al. 

(2005)...............................................................................................................................................174 

Fig. 4.1. Simplified model setup for initial geochemical model ....................................................................200 

Fig. 4.2. Time evolution of total inorganic carbon (TIC) (left) and pH (right) at CO2 intrusion 

location ............................................................................................................................................216 

Fig. 4.3. Time evolution of gas saturation degree at CO2 intrusion location..................................................216 

Fig. 4.4. Spatial distribution of saturation degree of CO2 gas after 100 years of intrusion ............................217 

Fig. 4.5. Spatial distribution of total inorganic carbon (TIC) (mol/L) after 100 years of intrusion ...............217 

Fig. 4.6. Spatial distribution of pH after 100 years of intrusion.....................................................................218 

Fig. 4.7. Total inorganic carbon (left) and pH (right) along the x-axis at y = 0 m, for Coastal Plain 

Sandstone.........................................................................................................................................219 

Fig. 4.8. Calcite volume fraction change profiles along x-axis at y = 0 m, for Coastal Plain 

Sandstone.........................................................................................................................................219 

Fig. 4.9. Spatial distribution of total aqueous Pb after 100 years of intrusion ...............................................220 

Fig. 4.10. Total aqueous Pb concentration profile along x-axis at y = 0 m......................................................221 

Fig. 4.11. Surface complex of Pb on the strong (left) and weak site (right) of illite along x-axis at y 

= 0 m for Coastal Plain Sandstone...................................................................................................222 

Fig. 4.12. Relative change of total Pb surface complexes concentration along the x-axis at y = 0 m 

for Coastal Plain Sandstone. ............................................................................................................222 

Fig. 4.13. Galena volume fraction change profiles along x-axis at y = 0 m, for Coastal Plain 

Sandstone.........................................................................................................................................224 

Fig. 4.14. Spatial distribution of total aqueous As concentration after 100 years of intrusion.........................225 

Fig. 4.15. Total aqueous As concentration profiles along the x-axis at y = 0 m, for Coastal Plain 

Sandstone.........................................................................................................................................225 

Fig. 4.16. Surface complex Ill _HAsO3

-

 (left) and Ill _H2AsO3 (right) profiles along the x-axis at y 

= 0 m, for Coastal Plain Sandstone..................................................................................................226 

Fig. 4.17. Relative change of total As surface complexes concentration (left) and arsenopyrite 

volume fraction change (right) along the x-axis at y = 0 m for Coastal Plain Sandstone. ...............226 

Fig. 4.18. Galena volume fraction change profiles along x-axis at y = 0 m 0 at different times in a 

sensitivity run without adsorption/desorption (left), and comparison of galena volume 

fraction change profile along x at y=0 after 100 years of CO2 intrusion with base model 

(right). ..............................................................................................................................................230 

Fig. 4.19. Aqueous Pb concentration profile along x at y=0 at different times in a sensitivity run 

without adsorption/desorption (left), and comparison of model results for total aqueous 

Pb concentration profile along x at y=0 after 100 years of CO2 intrusion with base model 

(right). ..............................................................................................................................................231 

Fig. 4.20. Aqueous As concentration profile along x at y=0 at different time in a sensitivity run 

without adsorption/desorption (left) and comparison of model results of As concentration 

profile along x at y=0 after 100 years of CO2 intrusion with base model (right).............................231 

Fig. 4.21. Comparison of model results for sensitivity case without adsorption/desorption and base 

model, showing aqueous Pb (left) and As (right) concentration profiles along x at y=0 

after 1 year of CO2 intrusion............................................................................................................232 
 

xiv 

Fig. 4.22. Galena volume fraction change profiles along x-axis at y = 0 m 0 at different times in a 

sensitivity run with lower site density for illite and comparison of galena volume fraction 

change profile along x at y=0 after 100 years of CO2 intrusion with base model (right).................235 

Fig. 4.23. Aqueous Pb concentration profile along x at y=0 at different times in a sensitivity run 

with lower site density on illite........................................................................................................235 

Fig. 4.24. Time evolution of total aqueous Pb concentration at the intrusion location (left) and 

comparison of model results for total aqueous Pb concentration profile along x at y=0 

after 100 years of CO2 intrusion in sensitivity and base model (right). ...........................................236 

Fig. 4.25. Aqueous As concentration profile along x at y=0 at different time in a sensitivity run with 

lower site density and comparison of model results of As concentration profile along x at 

y=0 after 100 years of CO2 intrusion with base model (right). ........................................................236 

Fig. 4.26. Computed As concentration after 100 years of CO2 intrusion in a sensitivity run (logK = -

3) and base model (logK = -2.12) ....................................................................................................238 

Fig. 4.27. Surface complex Ill _HAsO3

-

 (left) and Ill _H2AsO3 (right) profiles along the x-axis at y 

= 0 m, in a sensitivity run (logK = -3) and base model (logK = -2.12)............................................238 

Fig. 4.28. Computed galena volume fraction change (left) and (right) total adsorbed Pb change after 

100 years of CO2 intrusion with different kinetic-rate constant.......................................................240 

Fig. 4.29. Sensitivity of computed Pb concentration after 100 years of CO2 intrusion to kinetic-rate 

constant (k) for simulations without considering sorption...............................................................241 

Fig. 4.30. Computed As concentration after 100 years of CO2 intrusion in sensitivity runs with 

different kinetic-rate constant (k).....................................................................................................242 

Fig. 4.31. Sensitivity of computed As concentration after 100 years of CO2 intrusion to rate 

constant (k) for simulations without considering sorption...............................................................242 

Fig. 4.32. Computed total adsorbed Pb change (left) and galena volume fraction change (right) after 

100 years of CO2 intrusion with different logKPbS(aq).......................................................................244 

Fig. 4.33. Computed total aqueous Pb concentration after 100 years of CO2 intrusion with different 

logKPbS(aq).........................................................................................................................................244 

Fig. 4.34. Computed total adsorbed Pb (left) and aqueous Pb concentration (right) after 100 years of 

CO2 intrusion in sensitivity run (logK galena = -13.97) and base model (logK galena = -

14.4).................................................................................................................................................246 

Fig. 4.35. Computed aqueous As concentration after 100 years of CO2 intrusion in a sensitivity run 

(logK arsenopyrite = 55.16) and base run (logK arsenopyrite = 55.66)...........................................247 

Fig. 4.36. Sensitivity of total aqueous Pb concentration after 100 years along the x-axis at y = 0 m 

to the initial volume fraction of galena. ...........................................................................................248 

Fig. 4.37. Sensitivity of total aqueous As concentration after 100 years along the x-axis at y = 0 m 

to the initial volume fraction of arsenopyrite...................................................................................249 

Fig. 4.38. Sensitivity of Pb concentration to initial partial pressure of CO2. The figure on the right is 

the same as the one on the left but in semi-log scale. ......................................................................252

Fig. 4.39. Total Pb concentration on adsorption sites (left) and concentration of illw_OPb+

 (right) 

along x-axis at y = 0 m in the base and sensitivity run. ...................................................................252 

Fig. 4.40. Galena volume fraction change (left) and pH (right) along x-axis at y = 0 m in the base 

and sensitivity run............................................................................................................................253 

Fig. 4.41. Sensitivity of As concentration to initial partial pressure of CO2. ...................................................253 

Fig. 4.42. Sensitivity of CO2 gas saturation degree (left) and total inorganic carbon (TIC) to the 

depth of aquifer................................................................................................................................255 
 

xv 

Fig. 4.43. Sensitivity of pH to the depth of aquifer..........................................................................................255 

Fig. 4.44. Sensitivity of Pb (left) and As concentration (right) to the depth of aquifer....................................255 

Fig. 4.45. Spatial distribution of saturation of CO2 in a sensitivity run with flow rate of 1m/year 

after 100 years of intrusion ..............................................................................................................256 

Fig. 4.46. Spatial distribution of TIC in a sensitivity run with flow rate of 1m/year after 100 years of 

intrusion ...........................................................................................................................................257 

Fig. 4.47. Comparison of CO2 gas saturation degree (left) and TIC (right) along x-axis at y = 0 m in 

the sensitivity case and the base run ................................................................................................257 

Fig. 4.48. Evolution of pH at different times (left) along the x-axis at y = 0 m where groundwater 

flow velocity is 1m/year, and sensitivity of pH to groundwater flow velocity after 100 

years (right)......................................................................................................................................258 

Fig. 4.49. Total aqueous Pb concentration at different times (left) along the x-axis at y = 0 m where 

groundwater flow velocity is 1m/year, and sensitivity to groundwater flow velocity after 

100 years (right)...............................................................................................................................258 

Fig. 4.50. Total aqueous As concentration at different times (left) along x-axis at y = 0 m where 

groundwater flow velocity is 1m/year and sensitivity to groundwater flow velocity after 

100 years (right)...............................................................................................................................258 

Fig. 4.51. Spatial distribution of saturation for CO2 in a sensitivity run with CO2 intrusion rate of 

6× 10-4 kg/s after 100 years of intrusion ..........................................................................................260

Fig. 4.52. Spatial distribution of TIC in a sensitivity run with CO2 intrusion rate of 6× 10-4 kg/s 

after 100 years of intrusion ..............................................................................................................260 

Fig. 4.53. Comparison of CO2 gas saturation degree (left) and dissolved CO2 mass fraction (right) 

along x-axis at y = 0 m in the sensitivity and base run. ...................................................................261 

Fig. 4.54. pH evolution at different times (left) along the x-axis at y = 0 m for CO2 intrusion rate of 

6×10-4 kg/s, and sensitivity of pH to CO2 intrusion rate. .................................................................261 

Fig. 4.55. Total aqueous Pb concentration at different times (left) along the x-axis at y = 0 m for 

CO2 intrusion rate of 6×10-4 kg/s, and sensitivity of Pb concentration to CO2 intrusion 

rate. ..................................................................................................................................................262 

Fig. 4.56. Total aqueous As concentration at different times (left) along the x-axis at y = 0 m for 

CO2 intrusion rate of 6×10-4 kg/s, and sensitivity of As concentration to CO2 intrusion 

rate. ..................................................................................................................................................262 

Fig. 4.57. pH profiles (left) along the x-axis at y = 0 m at different times for St Peter sandstone, and 

comparison of pH computed for Coastal Plain Sandstone and St Peter Sandstone (right) ..............266 

Fig. 4.58. Volume fraction of calcite (left) and dolomite (right) along x-axis at y = 0 m ................................267 

Fig. 4.59. Total aqueous Pb concentration profiles (left) along x-axis at y = 0 m at different times 

for St Peter Sandstone, and comparison of computed Pb concentration for Coastal Plain 

Sandstone and St Peter Sandstone with adsorption/desorption (right).............................................268 

Fig. 4.60. Comparison of total Pb concentration on adsorption sites in mol/L for Coastal Plain 

Sandstone and St Peter Sandstone (left), and relative change in concentration on 

adsorption sites for St Peter Sandstone at 50 and 100 years (right) .................................................269 

Fig. 4.61. Total aqueous As concentration profiles (left) along x-axis at y = 0 m at different times 

for St Peter Sandstone, and comparison of computed As concentration for Coastal Plain 

Sandstone and St Peter Sandstone (right) ........................................................................................269 

Fig. 4.62. Comparison of total As concentration on adsorption sites in mol/L for Coastal Plain 

Sandstone and St Peter Sandstone (left), and relative change in concentration on 

adsorption sites for St Peter Sandstone at 50 and 100 years (right) .................................................270 
 

xvi 

Fig. 4.63. Total aqueous Pb (left) and As (right) concentration profiles (left) along x-axis at y = 0 m 

at different times for St Peter Sandstone obtained by a simulation without

adsorption/desorption.......................................................................................................................270 

Fig. 4.64. pH profiles (left) along x-axis at y = 0 m at different times for Dolomitic Limestone, and 

comparison of computed pH with Coastal Plain Sandstone (right). ................................................275 

Fig. 4.65. Comparison of calcite volume fraction change for Coastal Plain Sandstone and Dolomitic 

Limestone. .......................................................................................................................................275 

Fig. 4.66. Total aqueous Pb concentration profiles (left) along x-axis at y = 0 m at different times 

for Dolomite Limestone, and comparison of computed Pb concentration with Coastal 

Plain Sandstone (right).....................................................................................................................275 

Fig. 4.67. Comparison of total Pb concentration on adsorption sites (left) and change (right) for 

Coastal Plain Sandstone and Dolomitic Limestone. ........................................................................276 

Fig. 4.68. Comparison of surface complex illw_OPb+

 (left) and smes_oPb+for Coastal Plain 

Sandstone and Dolomitic Limestone. ..............................................................................................276 

Fig. 4.69. Total aqueous As concentration profiles (left) along x-axis at y = 0 m at different times 

for Dolomitic Limestone, and comparison of computed As concentration with Coastal 

Plain Sandstone (right).....................................................................................................................276 

Fig. 4.70. pH profiles along x-axis at y = 0 m, for oxidized Coastal Plain Sandstone. ....................................280 

Fig. 4.71. Surface complex of Pb on the strong (left) and weak site (right) of illite along x-axis at y 

= 0 m for oxidized Coastal Plain Sandstone. ...................................................................................281 

Fig. 4.72. Relative change of total Pb concentration on adsorption sites (left) and cerussite volume 

fraction change (right) profiles, along x-axis at y = 0 m (left) for oxidized Coastal Plain 

Sandstone.........................................................................................................................................281 

Fig. 4.73. Total aqueous Pb concentration profiles (left) along x-axis at y = 0 m at different times 

for oxidized C ..................................................................................................................................281 

Fig. 4.74. Close-up view of spatial distribution of CO2 gas saturation after 100 years of CO2

intrusion in 3D model with CO2 leakage rate of 7.5×10-5 kg/s. .......................................................285 

Fig. 4.75. Profiles CO2 gas saturation at x= 95 m, x =105 m, x = 115 m, and x = 125 m after 100 

years of CO2 intrusion in the 3D model with CO2 leakage rate of 7.5×10-5 kg/s.............................285 

Fig. 4.76. Close-up view of spatial distribution of Total Inorganic Carbon (TIC) after 100 years of 

CO2 intrusion in 3D model with CO2 leakage rate of 7.5×10-5 kg/s. ...............................................286 

Fig. 4.77. Profiles of concentration of Total Inorganic Carbon (TIC) at x= 95 m, x =105 m, x = 115 

m, and x = 125 m after 100 years of CO2 intrusion in the 3D model with CO2 leakage rate 

of 7.5×10-5 kg/s. ...............................................................................................................................286 

Fig. 4.78. Close-up view of spatial distribution of pH in 3D model after 100 years of CO2 intrusion 

in the 3D model with CO2 leakage rate of 7.5×10-5 kg/s. ................................................................287 

Fig. 4.79. Profiles of pH at x= 95 m, x =105 m, x = 115 m, and x = 125 m after 100 years of CO2

intrusion in the 3D model with CO2 leakage rate of 7.5×10-5 kg/s. .................................................287 

Fig. 4.80. Close-up view of spatial distribution of total aqueous Pb concentration after 100 years of 

CO2 intrusion in the 3D model with CO2 leakage rate of 7.5×10-5 kg/s...........................................288 

Fig. 4.81. Profiles of total aqueous Pb concentration at x= 95 m, x =105 m, x = 115 m, and x = 125 

m after 100 years of CO2 intrusion in the 3D model with CO2 leakage rate of 7.5×10-5

kg/s...................................................................................................................................................288 

Fig. 4.82. Close-up view of spatial distribution of total aqueous As concentration after 100 years of 

CO2 intrusion in the 3D model with CO2 leakage rate of 7.5×10-5 kg/s...........................................289 
 

xvii 

Fig. 4.83. Profiles of total aqueous As concentration at x= 95 m, x =105 m, x = 115 m, and x = 125 

m after 100 years of CO2 intrusion in the 3D model with CO2 leakage rate of 7.5×10-5

kg/s...................................................................................................................................................289 

Fig. 4.84. Spatial distribution of Total Inorganic Carbon (TIC) after 100 years of CO2 intrusion in 

3D model with CO2 leakage rate of 7.5×10-5 kg/s. ..........................................................................290 

Fig. 4.85. Spatial distribution of pH after 100 years of CO2 intrusion in 3D model with CO2 leakage 

rate of 7.5×10-5 kg/s. ........................................................................................................................290 

Fig. 4.86. Spatial distribution of total aqueous Pb concentration after 100 years of CO2 intrusion in 

3D model with CO2 leakage rate of 7.5×10-5 kg/s. ..........................................................................291 

Fig. 4.87. Spatial distribution of total aqueous As concentration after 100 years of CO2 intrusion in 

3D model with CO2 leakage rate of 7.5×10-5 kg/s. ..........................................................................291 

Fig. 4.88. Total aqueous Pb concentration along x-axis at y = 0 m and different aquifer depths (z = 

0, -5 and -10 m) for 3D model and total aqueous Pb concentration along x-axis at y = 0 m 

for 2D model....................................................................................................................................292 

Fig. 4.89. Total aqueous As concentration along x-axis at y = 0 m and different aquifer depth (z = 0, 

-5 and -10 m) for 3D model and total aqueous As concentration along x-axis at y = 0 m 

for 2D model....................................................................................................................................292 

Fig. 4.90. Spatial distribution of CO2 gas saturation after 100 years of CO2 intrusion in 3D model 

with flow rate of 1 m/y.....................................................................................................................295 

Fig. 4.91. Profiles of CO2 gas saturation at different locations after 100 years of CO2 intrusion in 

3D model with flow rate of 1 m/y....................................................................................................295 

Fig. 4.92. Spatial distribution of total inorganic carbon (TIC) after 100 years of CO2 intrusion in 3D 

model with flow rate of 1 m/y..........................................................................................................296 

Fig. 4.93. Total inorganic carbon (TIC) y-z profile at different locations after 100 years of CO2

intrusion in 3D model with flow rate of 1 m/y.................................................................................296 

Fig. 4.94. Spatial distribution of pH after 100 years of CO2 intrusion in 3D model with flow rate of 

1 m/y. ...............................................................................................................................................297 

Fig. 4.95. Profiles of pH at different locations after 100 years of CO2 intrusion in 3D model with 

flow rate of 1 m/y.............................................................................................................................297 

Fig. 4.96. Spatial distribution of total aqueous Pb concentration after 100 years of CO2 intrusion in 

3D model with flow rate of 1 m/y....................................................................................................298 

Fig. 4.97. Profiles of total aqueous Pb concentration at different locations after 100 years of CO2

intrusion in 3D model with flow rate of 1 m/y.................................................................................298 

Fig. 4.98. Spatial distribution of total aqueous As concentration after 100 years of CO2 intrusion in 

3D model with flow rate of 1 m/y....................................................................................................299 

Fig. 4.99. Profiles of total aqueous As concentration at different locations after 100 years of CO2

intrusion in 3D model with flow rate of 1 m/y.................................................................................299 

Fig. 4.100. Total aqueous Pb concentration along x-axis at different aquifer depths (z = 0, -5 and -10 

m) for 3D model and total aqueous Pb concentration along x-axis for 2D model (both 

models have a flow rate of 1 m/y). Top figure shows x-axis profiles at y = 0 m, bottom 

figure shows profiles y = 50 m.........................................................................................................300 

Fig. 4.101. Total aqueous As concentration along x-axis at different aquifer depths (z = 0, -5 and -10 

m) for 3D model and total aqueous As concentration along x-axis for 2D model (both 

models have a flow rate of 1 m/y). Top figure shows x-axis profiles at y = 0 m, bottom 

figure shows profiles at y = 50 m.....................................................................................................301 
 

xviii 

Fig. 4.102. Spatial distribution of CO2 gas saturation after 100 years of CO2 intrusion in 3D model 

with CO2 leakage rate of 6×10-4 kg/s. ..............................................................................................304 

Fig. 4.103. Profiles of CO2 gas saturation at different locations after 100 years of CO2 leakage in the 

3D model with CO2 intrusion rate of 6×10-4 kg/s. ...........................................................................304 

Fig. 4.104. Spatial distribution of TIC after 100 years of CO2 intrusion in 3D model with CO2

leakage rate of 6×10-4 kg/s...............................................................................................................305 

Fig. 4.105. Profiles of Total Inorganic Carbon (TIC) at different locations after 100 years of CO2

intrusion in the 3D model with CO2 leakage rate of 6×10-4 kg/s. ....................................................305 

Fig. 4.106. Spatial distribution of pH after 100 years of CO2 intrusion in 3D model with CO2 leakage 

rate of 6×10-4 kg/s. ...........................................................................................................................306 

Fig. 4.107. Profiles of pH at different locations after 100 years of CO2 intrusion in the 3D model with 

CO2 leakage rate of 6×10-4 kg/s. ......................................................................................................306 

Fig. 4.108. Spatial distribution of total aqueous Pb concentration after 100 years of CO2 intrusion in 

3D model with CO2 leakage rate of 6×10-4 kg/s. .............................................................................307 

Fig. 4.109. Profiles of total aqueous Pb concentration at different locations after 100 years of CO2

intrusion in the 3D model with CO2 leakage rate of 6×10-4 kg/s. ....................................................307 

Fig. 4.110. Spatial distribution of total aqueous As concentration after 100 years of CO2 intrusion in 

3D model with CO2 leakage rate of 6×10-4 kg/s. .............................................................................308 

Fig. 4.111. Profiles of total aqueous As concentration at different locations after 100 years of CO2

intrusion in the 3D model with CO2 leakage rate of 6×10-4 kg/s. ....................................................308 

Fig. 4.112. Total aqueous Pb concentration along x-axis at different aquifer depths (z = 0, -5 and -10 

m) for 3D model and total aqueous Pb concentration along x-axis for 2D model (both 

models have a CO2 intrusion rate of 6×10-4 kg/s). Top figure shows x-axis profiles at y = 

0 m, bottom figure shows profiles y = 50 m. ...................................................................................309 

Fig. 4.113. Total aqueous As concentration along x-axis at different aquifer depths (z = 0, -5 and -10 

m) for 3D model and total aqueous As concentration along x-axis for 2D model (both 

models have a CO2 intrusion rate of 6×10-4 kg/s). Top figure shows x-axis profiles at y = 

0 m, bottom figure shows profiles y = 50 m. ...................................................................................310 

Fig. 5.1. Map of the Albani Hills region showing major drainage basins and locations of water 

samples from Chiodini and Frondini (2001)....................................................................................323 
 

xix 

List of Tables 

Table 3.1. Characteristics (in weight percent, dry basis) of coals studied by Kolker et al. (2002) .....................20 

Table 3.2. Sequential selective leaching procedure to characterize the distribution of trace elements 

in the constituent components of coal (after Kolker et al., 2002) ......................................................20 

Table 3.3. Distribution (in %) of iron and selected trace elements in coal (from Kolker et al., 2002) ...............21 

Table 3.4. Molar ratio of trace metals (Me) in relation to As in pyrite compared to their molar 

abundance ratio in a bulk sample of Ohio 5/6/7 Coal. R2

 denotes correlation...................................22 

Table 3.5. Sequential selective leaching procedure to characterize the distribution of trace elements 

in marine sediments (after Huerta-Diaz and Morse, 1992)................................................................22 

Table 3.6. Potential mineral hosts for hazardous inorganic constituents under reducing conditions..................37 

Table 3.7. Values of analytical concentrations of chemical constituents at maximum frequency and 

associated calculated values of selected species in more than 38,000 potable 

groundwaters......................................................................................................................................44 

Table 3.8. Hazardous inorganic constituents with EPA-specified Maximum Contaminant Levels 

(MCLs) ..............................................................................................................................................49 

Table 3.9. Percentage of samples with measurable concentrations of hazardous inorganic 

constituents in groundwater samples analyzed with the most sensitive analytical method ...............56 

Table 3.10. Scope of modifications and augmentations to thermodynamic database...........................................67 

Table 3.11. Errors introduced through inadequate preservation of sample integrity deficiencies. .......................71 

Table 3.12. Input constraints for EQ3/6 species distribution runs conducted on groundwater analyses 

from the NWIS Database...................................................................................................................88 

Table 3.13. Summary of minerals identified as controlling the thermodynamic activity of selected 

hazardous constituents in potable groundwaters..............................................................................135

Table 3.14. Input specifications for base case EQ3 runs to calculate the equilibrium concentrations of 

hazardous inorganic constituents in groundwater at 25°C as a function of P(CO2). SS = 

Solid Solution. .................................................................................................................................141 

Table 3.15. Uncertainties associated with geochemical model assumptions ......................................................145 

Table 3.16. Uncertainties in geochemical model parameters..............................................................................148

Table 3.17. Uncertainties in chemical analyses of potable groundwaters...........................................................150 

Table 3.18. Uncertainties relating to environmental variability in sampled NWIS groundwater 

compositions ....................................................................................................................................152 

Table 3.19. Estimated Uncertainties contributing to the Aggregate Uncertainty in Log K of the 

Solubility Product of the Hazardous Constituent Mineral Host1

......................................................154 

Table 3.20. Variation of key dependent compositional parameters in groundwaters with variation in 

CO2 partial pressure .........................................................................................................................158 

Table 3.21. Concentration of aqueous species of hazardous constituents in aqueous solution in 

equilibrium with specified host minerals as a Function of CO2 partial pressure..............................160 

Table 3.22. Matrix clay mineralogy....................................................................................................................175 

Table 3.23. Representation of the mineralogy of hazardous inorganic substances in an arenaceous 

aquifer host rock ..............................................................................................................................178 

Table 3.24. Representative mineralogies of aquifer host rocks used in reactive chemical transport 

simulations.......................................................................................................................................183 
 

xx 

Table 4.1. Hydrodynamic parameters ...............................................................................................................201 

Table 4.2. Summary of sensitivity cases...........................................................................................................204 

Table 4.3. Mineral volume fractions, possible secondary minerals, and kinetic properties for a 

Coastal Plain Sandstone. Kinetic parameters from Xu et al. (2006)................................................207 

Table 4.4. Initial total aqueous concentration of major constituents obtained by initial equilibrium 

run for Coastal Plain Sandstone and analytical concentration at the maximum frequency 

in the histogram of 38,500 potable groundwaters and saturation indices of some major 

minerals............................................................................................................................................209 

Table 4.4a. Comparison of normalized saturation indices of some major minerals from the initial 

equilibrium run for Coastal Plain Sandstone with those calculated for potable 

groundwater with analytical concentrations at the maximum frequency in 38,500 potable 

groundwaters....................................................................................................................................212 

Table 4.5. Initial chemical composition of trace elements of groundwater from the TOUGHREACT 

equilibrium simulation in comparison .............................................................................................213 

Table 4.6. Concentrations of aqueous species of Pb.........................................................................................214 

Table 4.7. Speciation of aqueous species of As ................................................................................................214 

Table 4.8. Mass distribution (mol/L solution) of Pb and As on solid (as an essential component in 

minerals or adsorbed on mineral surfaces) and liquid phase in two-phase zone at initial 

(t=0) and final time (t=100 years)....................................................................................................228 

Table 4.9. Mass distribution (%) of Pb and As on solid (as essential an component in minerals or 

adsorbed on mineral surfaces) and liquid phase in two-phase zone at initial (t=0) and final 

time (t=100 years)............................................................................................................................228 

Table 4.10. Concentration (in mol/L) of some Pb aqueous species calculated by different dissociation 

constants of PbS(aq) at initial condition with logP(CO2) = -2.3 ......................................................244 

Table 4.11. Concentration (in mol/L) of some Pb aqueous species after initial equilibrium in base 

model and in the sensitivity run .......................................................................................................246 

Table 4.12. Initial chemical composition (mol/L) of groundwater in equilibrium with partial pressures 

of -2.3 (base model) and -1.5 (sensitivity run) in Log P(CO2).........................................................251 

Table 4.13. Mineral volume fractions, possible secondary minerals, and kinetic properties for a St 

Peter Sandstone................................................................................................................................264 

Table 4.14. Initial chemical composition (mol/L) of groundwater in equilibrium with Coastal Plain 

Sandstone and St Peter Sandstone ...................................................................................................265 

Table 4.15. Mineral volume fractions, possible secondary minerals, and kinetic properties for a 

Dolomitic Limestone .......................................................................................................................272 

Table 4.16. Initial chemical composition (mol/L) of groundwater in equilibrium with Coastal Plain 

Sandstone and Dolomitic Limestone. ..............................................................................................273 

Table 4.17. Mineral volume fractions, possible secondary minerals, and kinetic properties for an 

oxidized Coastal Plain Sandstone aquifer........................................................................................278 

Table 4.18. Chemical composition (mol/L) of groundwater in equilibrium with Coastal Plain 

Sandstone aquifer under reducing and oxidizing and conditions.....................................................279 
 

xxi 

List of Acronyms and Abbreviations 

AA Atomic Adsorption Spectroscopy 

As Arsenic 

ASR Aquifer Storage and Recovery 

Ba Barium 

Cd Cadmium 

EPA Environmental Protection Agency 

ICP-MS Inductively Coupled Plasma with Mass Spectroscopy 

Hg Mercury 

LBNL Lawrence Berkeley National Laboratory 

NAA Neutron Activation Analysis 

NASCENT Natural Analogs for the Storage of CO2 in the geological ENvironmenT 

NURE National Uranium Resource Evaluation 

MCL Maximum Contaminant Level 

MSW Municipal Solid Waste 

NGS National Geochemical Survey 

NWIS National Groundwater Information System 

Pb Lead 

Sb Antimony 

SDWR Secondary Drinking Water Regulation 

Se Selenium 

SI Saturation Index 

SS Solid Solution 

TIC Total Inorganic Carbon 

TDS Total Dissolved Solids 

USDW United States Drinking Water 

U Uranium 

XPS X-ray Photo electron Spectroscopy 

XAS X-ray Absorption Spectroscopy 
 

xxii 

List of Referenced Minerals and Their Chemical Formulas

Name Formula 

Albite-low NaAlSi3O8

Akdalaite (Al2O3)4·(H2O) 

Alstonite BaCa(CO3)2

Anglesite PbSO4

Anhydrite CaSO4

Ankerite Ca(Mg0.3Fe0.7)(CO3)2

Antimonselite Sb2Se3

Antimony Sb 

Apatite Ca5(OH)(PO4)3

Arsenic As 

Arsenic Oxide As2O5

Arsenolite As2O3

Arsenopyrite FeAsS 

Barite BaSO4 

Berthierite FeSb2S4

Berzelianite Cu2Se 

Cadmoselite CdSe 

Calcite CaCO3

Cerussite PbCO3

Chlorite Mg2.5Fe2.5Al2Si3O10(OH)8

Clinochlore Mg5Al2Si3O10(OH)8

Cinnabar HgS 

Claudetite II As2O3

Clausthalite PbSe 

Cotunnite PbCl2

Crandallite CaAl3(PO4)2(OH)5·(H2O) 

Dawsonite NaAlCO3(OH)2

Dolomite CaMg(CO3)2

E-Fe2O3 Fe2O3

Ferrihydrite Fe10O14(OH)2 

Ferrarisite Ca5(AsO3OH)2(AsO4)2.9H2O 
 

xxiii 

Name Formula 

Ferroselite FeSe2

FeS,am FeS 

Fieldlerite Pb3(OH)2Cl4

Galena PbS 

Goethite FeO(OH) 

Green Rust Fe(II)4Fe(III)2(OH)(SO4).8H2O 

Greenockite CdS 

Greigite Fe3S4

Gudmundite FeSbS 

Guerinite Ca5(AsO3OH)2(AsO4)2.9H2O 

Haidingerite Ca(AsO3OH).H2O 

Hematite Fe2O3

Hornesite Mg3(AsO4)2.8H2O 

Hydrocerussite Pb(OH)2.(PbCO3)2

Illite K0.6Mg0.25Al1.8Al0.5Si3.5O10(OH)2

Johnbaumite Ca5(AsO4)3(OH) 

Johnbaumite-Pb Pb5(AsO4)3(OH) 

K-feldspar KAlSi3O8

Kaolinite Al2Si2O5(OH)4

Kermesite Sb2S2O 

Kerogen-OS (-CHOH)18(-CHCOOH)5(-CHCOOCH2CH3)6(-CHSH)10

Kullerudite NiSe2 

Lanarkite PbO.PbSO4

Laurionite PbClOH 

Lead Pb 

Lead Hydroxide Pb(OH)2

Lepidocrocite FeO(OH) 

Litharge PbO 

Lorretoite Pb7O6Cl2

Mackinawite FeS 

Maghemite Fe2O3

Magnesite MgCO3

Marcasite FeS2 
 

xxiv 

Name Formula 

Massicot PbO 

Matlockite PbO.PbCl2

Mendipite (PbO)2.PbCl2

Meta-cinnabar HgS 

Mimetite Pb5(AsO4)3(Cl) 

Naumannite Ag2Se 

Oligoclase CaNa4Al6Si14O40

Opal-CT SiO2

Orpiment As2S3

Paralaurionite PbOHCl 

Pararealgar AsS 

Penfieldite Pb2OHCl3

Pharmacolite CaHAsO4.2H2O 

Pharmacosiderite KFe4(AsO4)3(OH)4.6H2O 

Picropharmacolite Ca4Mg(HAsO4)2(AsO4)2.11H2O 

Pitticite Fe2(AsO4)(SO4)(OH).6H2O 

Plumbonacrite Pb10O(OH)6(CO3)6

Pyrite FeS2

Pyromorphite Pb5(PO4)3(Cl) 

Quartz SiO2 

Realgar AsS 

Schwertmannite Fe(III)16O16(OH)12(SO4)2.12H2O 

Scorodite FeAsO4.2H2O 

Senarmonite Sb2O3

Siderite FeCO3

Sphalerite ZnS 

Stibnite Sb2S3

Symplesite Fe3(AsO4)2.8H2O 

Smectite-Ca Ca0.145Mg0.26Al1.77Si3.97O10(OH)2

Smectite-Na Na0.290Mg0.26Al1.77Si3.97O10(OH)2

Smectite-low-Fe-Mg Ca0.02Na0.15K0.2Fe++0.29Fe+++0.16Mg0.9Al1.25Si3.75H2O2

Sphalerite ZnS 

Talc Mg3Si4O10(OH)2
 

xxv 

Name Formula 

Talmessite Ca2Mg(AsO4)2.2H2O 

Tiemannite HgSe 

Tooeleite Fe6(AsO3)4(SO4)(OH)4.4H2O 

Uraninite UO2

Valentinite Sb2O3

Weilite CaHAsO4

Yukonite Ca7Fe11(AsO4)9O10.24H2O 
 

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1. Motivation 

Proper site selection and management of CO2 storage projects will ensure that the risks to human 

health and the environment are low. However, a risk remains that CO2 could migrate from a deep 

storage formation to arrive in shallow groundwater resources. Figure 1.1 shows schematically 

such a scenario, where a local high-permeability pathway—such as a permeable fault or an open 

abandoned well—allows for CO2 to escape from depth and reach a shallow aquifer used for 

water supply. The buoyant CO2 will undergo phase transition along this path and will typically 

reach the shallow strata as a gas, which will partially or completely dissolve into the 

groundwater. This process is by itself not typically a concern to the water quality of an 

underground source of drinking water (USDW), but it will change the geochemical conditions in 

the aquifer and will cause secondary effects, mainly by changes in pH. 

Fig. 1.1. Schematic illustrating potential groundwater quality changes in response to CO2 leakage from deep 

storage sites 
 

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As CO2 migrates into a freshwater aquifer and dissolves into the aqueous phase, the total 

concentration of dissolved carbonate increases and causes a decrease in pH. The higher acidity of 

the water may lead to enhanced dissolution of many minerals, among these some that may 

contain hazardous constituents such as lead (Pb) or arsenic (As) as essential components. For 

example, Pb is an essential component in galena, which is lead sulfide, and As is an essential 

component in arsenopyrite, which is iron arsenic sulfide. If such minerals are present and 

dissolve, the increasing concentration of hazardous constituents can cause detrimental effects to 

groundwater quality, possibly to the extent that maximum allowable levels are exceeded. 

Another concern related to decreasing pH is the possible mobilization of hazardous constituents 

on adsorption sites (e.g., clays, iron oxyhydoxides, or sorbed material on sand coats) or ion 

exchange sites (e.g., smectitic clays). For example, dissolution of iron oxyhydroxides in response 

to CO2 injection may have released sorbed toxic metals into the saline water at the Frio Pilot Test 

site (Kharaka et al., 2006). The problem of mobilizing such hazardous constituents (Pb, As, and 

others such as barium (Ba), cadmium (Cd), mercury (hg), antimony (Sb), selenium (Se), 

uranium(U)) in response to CO2 intrusion is relevant for most freshwater aquifers, because these 

elements occur—in trace amounts—ubiquitously in soils, sediments, and aquifer rock throughout 

the world. 

To date, the potential geochemical effects of CO2 intrusion into USDWs, especially those 

currently exploited as sources of potable water, have not been systematically assessed. Wang and 

Jaffe (2004) conducted reactive-transport simulations considering the migration of CO2 into an 

aquifer with significant volume fraction of galena present as an essential mineral component. 

Their results suggest strongly increased Pb concentration levels because of galena dissolution, in 

the case of a poorly buffered aquifer to levels higher than the maximum contaminant level 

(MCL) for Pb defined by the U.S. Environmental Protection Agency (EPA). It should be noted, 

however, that the authors intended their study as an illustrative example, to demonstrate the 

potential effects of CO2 intrusion on water quality; their simulations do not necessarily represent 

realistic conditions expected to occur if CO2 were to penetrate a potable water aquifer. This is 

because their reactive-transport simulations involved very simplified geochemical systems and 

were based on extreme mineralogical conditions. Thus, while the simulation results of Wang and 
 

3 of 352

Jaffe (2004) point to a potential concern that needs to be addressed, it is not clear to date how 

severe and widespread the problem would be in realistic cases. 

To adequately analyze the potential vulnerability of our groundwater resources in the event of 

large-scale deployment of carbon capture and storage, the geochemical consequences of CO2

migration into current and future potable water sources requires further evaluation. A multiyear 

research project was initiated in October 2006 to address these issues, the first phase of which is 

summarized in this annual report. The objective of this first phase is to investigate the water 

quality changes in response to CO2 intrusion through development of a geochemical model that 

describes the complete chemistry of a range of specified shallow aquifer systems. This model is 

then used in a series of reactive-transport modeling simulations to evaluate transient responses to 

CO2 intrusion. The goal is to better understand, in a global sense, the vulnerability of 

representative and relevant aquifer types in the case of CO2 intrusion, thereby providing sound 

scientific information to regulators and the public. It is understood, however, that evaluation of 

specific sites will require additional simulation analysis considering the site-specific conditions, 

ideally to be supported by laboratory and field experiments. 
 

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2. Work Scope and Research Plan 

This report describes a systematic evaluation of the possible water quality changes in response to 

CO2 intrusion into aquifers currently used as sources of potable water in the United States. Such 

waters generally contain total dissolved solids concentrations far less that the maximum 

permitted 10,000 mg/L permitted in a USDW. Our goal is to develop a principal understanding 

that would allow us to evaluate which aquifer systems and regions of the United States may be 

vulnerable in case of CO2 intrusion. In particular, we need to understand, for a range of relevant 

aquifer conditions, the potential for hazardous constituents (such as heavy metals or As) to be 

mobilized by the ingress of CO2 and the distribution of these constituents in time and space. 

Ultimately, we need to determine the likelihood for drinking water standards to be exceeded. 

These issues are addressed by conducting reactive transport simulations for a variety of CO2-

release scenarios and aquifer conditions. A reactive transport model was developed for 

quantifying the effect of CO2 intrusion into a shallow aquifer, simulating the multiphase flow of 

CO2 and water, and predicting the fate and migration of trace hazardous constituents in the 

groundwater (Section 4). 

As in any numerical simulation study, reliable model predictions can only be achieved when 

model parameters are both realistic and representative, and when all relevant processes are 

adequately incorporated. In particular, the reactive transport model needs to (1) account 

adequately for the complexity in natural geochemical systems, (2) consider the relevant 

geochemical processes, (3) represent a wide range of geochemical conditions with respect to 

initial water chemistry and mineralogy, and (4) incorporate variability and uncertainty in relevant 

hydrogeological and geochemical parameters. To ensure adequate model configuration, we have 

selected the conditions modeled in the reactive transport simulations—e.g., initial abundance and 

distribution of these hazardous constituents, aquifer mineralogy, initial water chemistry, 

oxidation state—based on development of a comprehensive geochemical model, which included 

the rigorous evaluation of more than 38,000 groundwater quality analyses from aquifers 

throughout the United States, using a version of the EQ3/6 code (Section 3). This geochemical 

model development allowed us to draw some immediate quantitative conclusions regarding the 
 

5 of 352

potential vulnerability of groundwaters in the United States, for example with respect to the pH 

or Eh buffering capacity of most samples. The geochemical model also allowed preliminary 

estimates of the potential concentrations of hazardous constituents in equilibrium with elevated 

partial pressures of CO2 following intrusion. However, in order to explore spatial changes in 

aquifer chemistry under transient conditions following CO2 intrusion, we employed the reactive 

transport simulations in which was incorporated a suitable surface complexation model to 

describe adsorption processes. 

Based on the above discussion, the research work as described in this report has two main parts: 

(1) the development of the aquifer geochemical model detailed in Section 3, and (2) the 

systematic reactive transport simulations detailed in Section 4. Figure 2.1 depicts the information 

flow between these two parts. The key information gathered in Part (1), and provided to Part (2), 

is listed below: 

- Review, evaluation, and generation of thermodynamic and solubility data for all relevant 

minerals and aqueous species, and their incorporation into the reactive transport simulator 

- Geochemical evaluation of a large number of water quality analyses with measurable 

concentrations of hazardous inorganic constituents; determination of chemical potentials and 

thermodynamic controls for various relevant hazardous constituents; and evaluation of 

mineral assemblages influencing pH and Eh buffering capacity, as well as background levels 

of CO2 concentration 

- Review of representative mineralogies for principal aquifers typically exploited for potable 

water; and classification of these aquifers with respect to expected geochemical behavior; 

development of selected mineralogies for simulation study (considering major minerals and 

clays) 

- Identification of minor minerals that typically have small volume fraction, but are critical 

because they contain hazardous chemical constituents such as Pb and/or As; and specification 

of possible volume fraction ranges for these minor minerals 

- Review and evaluation of relevant thermodynamic parameters in support of a surface 

complexation model to describe adsorption and desorption, and ion exchange processes. 
 

6 of 352

The first step in Part (2) is to set up the reactive transport simulator in a single cell configuration 

with the above-described inputs, and then run the simulator until a secular equilibrium state is 

reached. This secular-equilibrium state establishes the initial distribution of the hazardous 

constituents in the aquifer, which is a necessary prerequisite for reactive transport simulations 

where the initial state is perturbed following CO2 intrusion. The initial secular geochemical 

equilibrium is generated using the same geochemical simulator TOUGHREACT (Xu et al., 

2006) employed for the reactive transport simulations (Section 4). The secular equilibrium 

calculation considers that the substance may be initially distributed (1) in the aqueous phase as 

ionic, molecular, and complex species (possibly occurring with differing oxidation states), (2) as 

adsorbed species on the surfaces of the minerals composing the aquifer rock, (3) as species in ion 

exchange sites, (4) in trace amounts in solid solution, and (5) as discrete minerals in which the 

substance is an essential component. As a check for the model and its thermodynamic 

parameters, the simulated initial concentrations of various species in the water are compared 

back to the range determined in the database evaluation (Section 4.4). 

3⁄4 Development of Geochemical Model

– What is the initial concentration of hazardous constituents in aquifers?

– Which trace minerals control the initial concentration of such 

constituents?

– What is the predicted concentration of hazardous constituents as a 

function of P(CO2)?

– What types of mineralogy (main minerals) should be considered to study a 

range of more or less vulnerable aquifers?

– What are the expected volumetric fractions of such trace minerals?

3⁄4 Development of Geochemical Model

– What is the initial concentration of hazardous constituents in aquifers?

– Which trace minerals control the initial concentration of such 

constituents?

– What is the predicted concentration of hazardous constituents as a 

function of P(CO2)?

– What types of mineralogy (main minerals) should be considered to study a 

range of more or less vulnerable aquifers?

– What are the expected volumetric fractions of such trace minerals?

3⁄4 Systematic Reactive Transport Model Analysis

– How are hazardous constituents initially distributed in the aquifer 

(aqueous phase, adsorbed, on ion exchange sites, in trace minerals)?

– What is the possible impact of CO2 intrusion on water quality, considering 

a wide range of hydrogeological and geochemical conditions?

– What are the controlling processes or parameters?

3⁄4 Systematic Reactive Transport Model Analysis

– How are hazardous constituents initially distributed in the aquifer 

(aqueous phase, adsorbed, on ion exchange sites, in trace minerals)?

– What is the possible impact of CO2 intrusion on water quality, considering 

a wide range of hydrogeological and geochemical conditions?

– What are the controlling processes or parameters?

Fig. 2.1. Main research steps and information flow between them 
 

7 of 352

The final step in Part (2) is to conduct the reactive transport simulations for a series of simulation 

cases. Starting with the specified initial geochemical conditions, these simulations predict the 

impact of CO2 intrusion into a fresh-water aquifer and determine the related changes to the host 

rock mineralogy and water chemistry. Various geochemical sensitivity cases are evaluated to (1) 

account for the wide range of conditions found in domestic potable aquifers and their host rocks, 

and (2) to encompass the uncertainty associated with geochemical processes and model 

parameters. In addition, we have varied certain hydrogeological aquifer conditions (such as the 

depth of the freshwater resource and the groundwater flow rate), have simulated different CO2

release scenarios (intrusion rate of CO2), and have conducted 2D versus 3D modeling to 

understand the impact of dimensionality. While including a wide range of relevant mineral 

phases in the reactive transport model, including their potential interactions, we have focused on 

two major hazardous constituents—Pb and As—when evaluating the detrimental effects of CO2

intrusion. Based on our modeling results, we can provide a better understanding of the expected 

vulnerability of relevant domestic aquifer types in the case of CO2 intrusion. As mentioned 

before, our broad sensitivity study, intended to provide results for a wide range of representative 

conditions, is a starting point for an essential next step, to conduct a detailed geochemical 

evaluation of selected storage sites incorporating the site-specific conditions and parameters. 

Note that all geochemical modeling studies conducted in this report assume the intrusion of pure 

CO2 into potable drinking water. It is possible, however, that leaking CO2 may carry organic 

compounds leached at depth; they may also contain co-injected trace contaminants, such as H2S. 

The conditions for this to occur, and the potential ramifications, are explored in future project 

phases. 

We also note that bacterial processes are not explicitly accounted for in our reactive transport 

modeling. Bacteria take advantage of non-equilibrium states to extract energy from electron 

transfer reactions where corresponding abiotic reactions, which would otherwise bring the 

chemical system into redox equilibrium, are kinetically hindered by a large activation energy 

barrier. The intrusion of CO2 into potable groundwater aquifers will not materially increase the 

opportunity for bacteria to extract energy from the system, and potential bacterial mechanisms 
 

8 of 352

that might have some small impact, such as those affecting sulfide mineral dissolution, have not 

been examined. Instead, we implicitly account for potential bacterial activity through sensitivity 

studies of the kinetics of redox reactions involving the dissolution respectively of galena and the 

solid solution of arsenopyrite in pyrite. 

In addition to geochemical modeling, we also report on natural and man-made natural analogues 

providing corroborative information about the potential water quality changes in response to CO2

intrusion into groundwater (Section 5). 
 

9 of 352

3. Geochemical Model Definition 

3.1 Basic Methodology 

In this section, a series of steps is described with the goal of developing a geochemical model for 

use in reactive chemical transport simulations described in Section 4. The fate of inorganic 

hazardous constituents in the presence of elevated CO2 levels, such as after intrusion of leaking 

CO2 from deep geological storage, ultimately depends on their initial abundance and distribution 

in the aquifer. Initially, they are distributed (1) in the aqueous phase as ionic, molecular, and 

complex species, (2) as adsorbed species on the surfaces of the minerals composing the aquifer 

rock, (3) as species in ion exchange sites (primarily in smectitic clays), and (4) in trace amounts 

in solid solution in detrital minerals, weathering products, chemical precipitates (e.g., carbonates 

and sulfates) and diagenetic minerals, and (5) possibly as discrete minerals in which the 

constituent is an essential component. These initial distributions must be characterized and 

quantified as a necessary prerequisite for conducting reactive transport simulations evaluating 

the mobilization of such constituents in the case of CO2 intrusion. This requirement is met 

through the compilation of pertinent thermodynamic parameters governing their distribution in 

the aquifer, e.g., the thermodynamic properties of both the minerals and their dissolved 

components in aqueous solution, ion exchange coefficients, adsorption coefficients, and 

coefficients defining solid solutions. Also required are the electrolyte, site complexation, ion 

exchange, and solid solution models appropriate to the problem at hand. 

The steps taken in developing the geochemical model included (1) a comprehensive review of 

the literature in order to identify mineral hosts controlling the concentrations of hazardous 

constituents in groundwaters, (2) development of an extensive thermodynamic database for 

relevant minerals and aqueous species and site complexation constants, (3) rigorous geochemical 

evaluation of more than 38,000 groundwater quality analyses from potable water aquifers 

throughout the United States, (4) evaluation of the natural abundances of hazardous constituents 

in sedimentary formations hosting potable water, and (5) a survey of the sedimentology and 
 

10 of 352

hydrology of the principal potable water aquifers throughout the United States. The key 

questions addressed are as follows: 

1. What minerals might be present that control the aqueous concentrations of hazardous 

constituents in potable groundwaters? 

2. How is the aqueous concentration of each hazardous constituent determined? Is the 

aquifer water saturated with respect to a particular mineral that contains that constituent 

as an essential component, or is it in equilibrium with that constituent only as a minor, 

non-essential component associated with other rock-forming minerals? 

3. What would be the mass (or volumetric) fractions of solubility-controlling minerals 

postulated to be present in the aquifer rocks? 

4. What is the framework and matrix mineralogy of typical aquifers exploited for potable 

water? What types of representative mineral compositions should be considered to study 

a range of more or less vulnerable aquifers? 

Figure 3.1 illustrates schematically the interrelated tasks required to address these questions, and 

their relationship to tasks performed with the reactive transport model analysis in Section 4. 

Question (1) is addressed in Section 3.2 through a review of the modes of occurrence for the 

chosen hazardous constituents in sedimentary rocks, including coal, for which an extensive and 

highly relevant literature is available. Question (2) is addressed in part through the geochemical 

evaluation of a large number of groundwater samples from the USGS National Groundwater 

Information System (NWIS) database (Sections 3.3 and 3.4). The technical objective of the 

groundwater evaluation is to identify the thermodynamic controls governing the concentrations 

of hazardous inorganic constituents in potable groundwaters samples taken throughout the 

United States. Identifying the thermodynamic controls is an indirect way of determining whether 

(and which) solubility-controlling minerals (i.e., galena possibly controlling the aqueous Pb 

concentrations) are present in the aquifer from which the sample is recovered. The distributionof-species code EQ3/6 (Wolery et al., 1993) was used in this study to calculate the saturation 

indices of minerals containing hazardous constituents as essential components in a selected set of 

suitable groundwater analyses downloaded from the NWIS database (Section 3.3.1). 
 

11 of 352

The reason for taking this indirect approach is that the mass fractions of the relevant hazardous 

constituents (i.e., the solubility-controlling phases) in the aquifer host rocks are very low (on the 

order of 10-4 - 10-6 or less), and few studies have been conducted that have unequivocally 

confirmed the presence of their mineral hosts, particularly in potable water aquifers. Direct 

means of identifying their presence and spatial distribution are only possible with high-resolution 

imaging techniques or other highly complex methodologies, an effort that is rarely undertaken 

and certainly not in a systematic manner. Therefore, in this study, we postulate the presence of 

solubility-controlling phases in the aquifer rocks through geochemical evaluation of groundwater 

chemical analyses containing specific data for several hazardous inorganic constituents of 

interest, and corroborated through independent studies reported in the literature (Section 3.4). 

Question (3) is addressed by consulting the National Geochemical Survey Database (USGS, 

2008) in which the analyzed concentrations of hazardous constituents present in surficial soils 

and sediments are tabulated (Section 3.3.3). This information was used as a proxy for 

corresponding concentrations in aquifer host rocks, expressed in mass fraction or volume 

fraction, and is needed because the thermodynamic analysis described above only identifies 

which phases may control the concentration of a hazardous constituent in solution, but cannot 

define the quantities present. (Saturation indices of minerals are intensive thermodynamic 

variables—i.e., variables that are independent of mass.) 

Finally, to answer Question (4), a variety of independent literature sources was used to obtain 

representative mineralogies for representative aquifer host rocks (Section 3.6). Integration of the 

bulk mineralogy with the mineralogy characterizing hazardous inorganic constituent mineral 

hosts, and normalizing to volume fractions, yields input suitable for TOUGHREACT. For the 

geochemical model to be realistic, further information is also required regarding the rock 

physical properties such as porosity and specific surface areas. 

Once the total mineralogy has been identified and quantified, a geochemical secular equilibrium 

model is constructed and tested for consistency with groundwater chemical analyses (Section 

4.4). The model must have attained a steady-state kinetic or secular equilibrium with respect to 

the major rock forming primary and secondary minerals, and equilibrium with respect to all 
 

12 of 352

minor and trace minerals that could define the aqueous equilibrium concentration of the 

hazardous constituents under study. This equilibrium state needs to be established not only with 

respect to the minerals containing hazardous inorganic constituents as essential components, but 

also with respect to those constituents on adsorption sites on primary and secondary host rock 

minerals, on ion exchange sites in clays, and optionally in solid solution in secondary minerals. 

The geochemical model code needed for this purpose requires a site complexation model with 

suitable adsorption constants for each inorganic constituent, for the surfaces of each mineral 

incorporated in the model, an ion-exchange model with suitable ion-exchange coefficients, and a 

suitable solid-solution model for trace substitution of inorganic substances in specified host 

minerals. In principle, the distribution of each hazardous constituent between the constituent host 

minerals can be compared with corresponding field data obtained in answer to Question (1), as a 

basis for partial model validation. Note that incorporation of all mechanisms for immobilization 

of hazardous constituents in/on solid phases of the host rock is not always necessary. For 

example, it was found that Pb+2 substitution into ion exchange sites in clays was not a significant 

sink for Pb in groundwater, when considering the the geochemical model for potable water 

aquifers generated in this study. 

Results from the secular equilibrium geochemical model used in Section 4.4 to generate the 

simulated aqueous-phase composition at the initial state (prior to CO2 intrusion) can be 

compared with the concentration ranges observed in NWIS groundwaters. If the chemical system 

has been adequately defined, the thermodynamic data used to construct the model are accurate, 

and (last, but not least) thermodynamic equilibrium is closely approached in the field, then a 

reasonable agreement should be obtained. The model can therefore be checked for consistency 

against the known ambient conditions and constitutes a further partial validation of the model. 

The final step shown in the flowchart in Figure 3.1 is to conduct the reactive transport 

simulations predicting the water quality impact in response to CO2 intrusion from an underlying 

storage reservoir (Sections 4.5 through 4.10). Starting with the equilibrated initial conditions, the 

reactive transport model allows for mobilization of relevant hazardous constituents from mineral 

dissolution as well as from adsorption and ion exchange sites. In such a model, homogeneous 
 

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(i.e. within a single phase, most commonly the aqueous phase) and heterogeneous (i.e. between 

different phases) chemical kinetics, gas-aqueous-phase mass transfer, and aqueous-phase 

transport play a role. The reactive-transport simulator used in this study, for the reactive transport 

simulations as well as for conducting secular equilibrium runs, is TOUGHREACT (Xu et al., 

2005), described in Section 4.1. The relevant thermodynamic data incorporated into this 

simulator have been derived from a comprehensive review of the published literature, mostly 

based on laboratory experiments, some estimated. Extensive revision and augmentation of the 

thermodynamic data were necessary for the purpose of this study (Section 3.4.2). 

Uncertainties associated with the geochemical model remain, primarily because the chemical 

thermodynamic and kinetic data to model such complex systems, even under ambient conditions, 

are incomplete or subject to large uncertainties. The physical and chemical response of a potable 

groundwater aquifer to elevated CO2 pressures has received only limited study in the laboratory, 

and then under conditions that are of limited relevance. It is possible that some physical 

processes and chemical reactions operative in the presence of high pressure CO2 have not been 

identified, and therefore the modeling could suffer from unrecognized omissions. Furthermore, 

the physical characteristics specified for aquifer conditions in this study are generalized, and 

aquifer host-rock heterogeneity is not taken into account. To remedy the related uncertainties, 

further laboratory investigations and field characterization are desirable. In the course of this 

study, uncertainties and knowledge gaps that could affect the findings of this evaluation are 

identified, and are summarized in Sections 3.7 and 4.10 of this report. 
 

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Note: HIC = Hazardous Inorganic Constituent, DL = Detection Limit, MCL = Maximum Contaminant Level 

Fig. 3.1. Flow chart describing research steps providing necessary information as input to reactive transport 

models 
 

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3.2 Identification of Mineral Hosts Controlling the Concentration 

of Hazardous Constituents 

A comprehensive review of the literature was conducted to characterize the phase relations 

expected for relevant hazardous constituents in shallow potable water aquifers, and to identify 

mineral component(s) best suited as references in the thermodynamic evaluation of water quality 

data. Emphasis is placed on phase relations expected under reducing conditions, as appears to be 

the case with the majority of groundwaters for which water quality data are available (Section 

3.4.4). 

The selection of trace elements was based on their immediate relevance to the study, i.e., 

considering the work of Wang and Jaffe (2004) regarding lead (Pb) and recognizing a recent 

study by Focazio et al. (2006), which showed that arsenic (As) and uranium (U) concentrations 

sometimes exceeded maximum permissible concentrations in domestic wells. Other elements 

studied include barium (Ba), cadmium (Cd), mercury (Hg), antimony (Sb), selenium (Se), and 

zinc (Zn). These elements, all of which except Zn are considered potentially hazardous, were 

selected because their chemical behavior is fairly well known, or can be correlated with other 

elements whose chemistry is well understood for the conditions of the study. While Zn is not 

particularly important as a hazardous constituent, its behavior is somewhat similar to Cd, but it is 

found at higher and more easily detectable concentrations, and can therefore serve as an 

interpretive tracer. 

Most of the selected elements, the exceptions being Ba and U, participate in the formation of 

chalcogenides, either as a reactive aqueous metal cation or complex in solution (i.e., Cd, Hg, Pb 

and Zn) or as a co-precipitant that substitutes or proxies for sulfide (S(-II)—i.e., As(-I), Sb(-I?) 

and Se(-II)). Ba(II) precipitates primarily as a sulfate, sometimes as a carbonate, whereas U in 

the reduced (IV) state commonly precipitates as the oxide, uraninite (UO2) and/or as the silicate, 

coffinite (USiO4), or more rarely as the titanate, brannerite (UTi2O6). 
 

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Reducing conditions in hydrothermal waters are enhanced through hydrolysis by SO2, which 

disproportionates below about 450°C, according to the reaction: 

4SO2 + 4H2O = 7H+ + 3SO4

-2 + HSand by hydrolysis of Fe(II) containing minerals, thus: 

3(FeO)ss + H2O = Fe3O4 + H2(aq) 

These reactions contribute to the aqueous precipitation of chalcogenides at temperatures between 

≈ 400 and 0°C. 

At temperatures above 450°C, the chalcophilic elements, Cd, Hg, Pb and Zn, tend to substitute in 

their oxide components in rock-forming minerals, except under unusual conditions such as the 

extreme reduction induced by hydrolysis by the FeO component of mafic minerals. For example, 

Pb+2 together with Al+3 tends to participate in coupled substitution for K+

 with Si+4 in potash 

feldspars (Stevenson and Martin, 1986) in a manner similar to that of Ba+2, which is of similar 

ionic radius (i.e., (Ba,Pb)-Al2Si2O8 for K-AlSi3O8), to preserve charge balance, or partitions as 

chloride complexes in residual hypersaline brines following solidification of acidic magmas. In 

contrast, As and Sb in the (V) state are likely to follow phosphate with substitution in apatite or 

other metamorphic phosphates (Wise and Loh, 1976). Selenium in the (IV) or (VI) states 

probably mimics S(IV) and S(VI), but further review is required to clarify its behavior at 

elevated temperatures (see Smith, 1963). The chemistry of U follows similarly with substitution 

of the oxidized form, U(VI), as in magmatic apatite. 

During subaerial weathering, many of the magmatically formed minerals are unstable in the 

presence of water and decompose, liberating the trace elements in their highest oxidation states. 

As, S, Sb, and Se tend to form oxy-anionic species, whose salts are soluble, and therefore tend to 

remain in solution, or are adsorbed on secondary minerals whose surfaces are positively charged, 

e.g., ferric oxides and oxy-hydroxides. Uranium is also released in its highest oxidation state, as 

UO2

+2, and Hg as Hg+2. Detrital and secondary products of weathering will undergo further 

alteration either immediately following shallow burial (authigenesis), or following prolonged 
 

17 of 352

deeper burial, where progressively increasing temperatures lead to further alteration and 

precipitation of secondary minerals (diagenesis). A critical aspect of both authigenesis and 

diagenesis is the role that entrapped organic matter plays as an electron donor in facilitating the 

reduction of As(V), S(VI), Sb(V), Se(IV,VI) and U(VI). Under authigenic conditions, bacteria 

utilize organic matter in the reduction of these species, together with Fe(III) and Mn(III and IV) 

to Fe(II) and Mn(II), respectively. Therefore, oxides such as hematite, goethite and ferrihydrite, 

which initially dominate iron minerals, are partially or completely reduced to secondary iron 

sulfides under subsurface conditions. Reduction of iron to sulfide is complicated by the 

formation of metastable sulfide intermediates such as greigite (Fe3S4) and mackinawite (FeS). 

During the formation of metastable precursor iron sulfides or pyrite, Hg, As, Sb and Se are 

sequestered and incorporated in solid solution, probably in a disordered state. Because the 

concentration of iron overwhelms the concentrations of these elements, they are substantially 

partitioned, possibly in metastable equilibrium, into the iron sulfide, the residue being adsorbed 

onto thiol groups in the organic matter as Hg is believed to behave (Benoit et al., 1999). 

With deeper burial and higher temperatures, organic matter undergoes thermolysis and 

maturation at temperatures between ~75–120°C (Tissot and Welte, 1978), which leads to the 

destruction of unstable thiol and amino groups on the organic matter, while accelerating the 

reduction of Fe(III) and Mn(III and IV) and S(VI). Diagenetic recrystallization of metastable iron 

sulfides to pyrite can lead to significant reduction in the extent of solid solution and expulsion of 

Hg, As, Sb and Se. Maturation of organic matter also causes the release of previously adsorbed 

trace metals to the groundwater, potentially assisted through complexation with carboxylates and 

amines. Increased acidity, due to the release of CO2 during maturation, also enhances the 

solubility of trace metals. Finally, increased salinity, either from trapped connate seawater or 

from the dissolution of evaporites, likewise increases the concentration of Cd, Hg, Pb and Zn 

through chloride complexing. Thus, several mechanisms are operative that lead to enhanced 

concentration of trace metals in deep groundwater, which can facilitate their transport to lower 

temperature regions. In aggregate, these processes can result in supersaturation and secondary 

precipitation of discrete phases where the affected elements are essential components. 

Circumstantial evidence in support of this phenomenon is presented below. 
 

18 of 352

Deep burial with temperatures rising to ~300°C can lead to further destruction of primary 

minerals, with the associated release of trace elements. Secondary pyrite formed under these 

conditions contains enhanced concentrations of Hg, As, Sb, and Se in solid solution, because the 

capacity to accept these nonstoichiometric elements in solid solution increases with temperature, 

and larger quantities become available due to alteration of the primary minerals. With erosional 

unroofing of deeply buried sediments and falling temperature, retrograde metamorphism takes 

place, and the initial metastable pyrite solid solution becomes unstable. Supersaturation with 

respect to secondary minerals containing the trace elements as essential components can then 

occur. However, it is more likely that ex-solution of secondary phases will occur within pyrite 

crystals, and therefore, the trace metals will remain sequestered by the pyrite, rather than being 

released to the groundwater and precipitating as discrete stoichiometric minerals. The scenarios 

just described are supported by field observations, but many of the mechanisms involved are not 

well understood, even in a qualitative sense. Nevertheless, a general understanding of the 

processes involved is essential for understanding how individual hazardous inorganic 

constituents are likely to behave following the ingress of high-pressure CO2 into shallow 

groundwaters. 

In the following paragraphs, we speculate on the behavior of each hazardous inorganic 

constituent and identify mineral component(s) as likely candidates in the thermodynamic 

evaluation of water quality data presented in Section 3.4. The thermodynamic activity of these 

components, defined in terms of corresponding mineral saturation indices (SI), can then be used 

to establish whether they might actually be present as discrete minerals in the aquifer host rocks 

components (i.e., if SI ≥ 0). If, however, SI ≤ 0, then that component is either present in solid 

solution in another mineral, or some other component is saturated with respect to a discrete 

mineral. The definition of SI is presented in Section 3.4.1. 

Little information is available in the literature concerning the distribution of the hazardous 

inorganic constituents of interest in shallow aquifer host rocks. However, extensive studies have 

been undertaken over many years to characterize the distributions of such constituents in the 

various mineral components of coals—e.g., see Finkelman (1981) and Kolker et al. (2002). The 
 

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reason for such an interest is that many trace elements are environmentally hazardous, and 

potential coal pretreatment processes to minimize their release to the atmosphere following 

combustion is of more than passing interest. It is obvious that coals differ markedly in 

composition from a typical shallow aquifer host rock. A typical arenaceous aquifer rarely 

contains more than 2 wt% of organic matter, in contrast to coal, which in some instances exceeds 

95 wt%. Also, the “ash” content of exploitable coal rarely exceeds 30 wt.%, whereas nearly 100 

wt.% of an aquifer host rock consists of inorganic material. However, the sustained reducing 

environment induced by organic matter in coal beds, and the duration of burial, allow 

sequestration and enrichment of hazardous constituents in discrete mineral hosts, such as 

chalcogenides, which are more readily identified and characterized in coal than in arenaceous 

sedimentary rocks. Furthermore, because of the incentive to study the distribution and mode of 

occurrence of hazardous constituents in coals for environmental reasons, a significant literature 

selection is available that can be used opportunistically to help define a comparable host 

mineralogy for hazardous constituents in potable water aquifers. It goes without saying that such 

an interpretation must be undertaken with full cognizance of the impact that the substantial 

compositional differences between coal and arenaceous sedimentary rocks have on the 

distributions of hazardous constituents between the various mineralogical components 

comprising each material. 

The Unites States Geological Survey recently conducted a comprehensive multi-element 

characterization of selected coals (Kolker et al., 2002). This study is invaluable in providing a 

qualitative basis for geochemical model development for the present project. Relevant 

information is summarized in Tables 3.1, 3.2 and 3.3, which show, respectively, the bulk 

composition and character of three coals, the sequential selective-leaching procedure involved in 

the separation of the principal components of the coals, and finally, the distribution of the 

selected inorganic constituents and iron in the three coals, as determined by the sequential 

selective leaching procedure. The pertinence of these findings in relation to the distributions of 

individual trace elements in shallow aquifer host rocks is provided in the following discussion. 
 

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Table 3.1. Characteristics (in weight percent, dry basis) of coals studied by Kolker et al. (2002) 

 Ohio #5, #6, #7 beds Wyodak, Cordero 

Mine, Gillette, WY 

North Dakota, Falkirk 

Mine, Underwood, ND 

Ash 9.93 8.13 14.64 

Pyritic Sulfur 1.42 0.05 0.28 

Organic Sulfur 1.24 0.37 0.56 

Sulfate Sulfur 0.02 0.01 0.14 

Moisture 2.33 25.81 35.88 

Rank Bituminous Sub-bituminous Lignite 

Table 3.2. Sequential selective leaching procedure to characterize the distribution of trace elements in the 

constituent components of coal (after Kolker et al., 2002) 

# Procedure Elements Extracted from: 

1 1N ammonium acetate (CH3COONH3) exchangeable sites, water soluble compounds, 

some carbonates 

2 3N hydrochloric acid (HCl) carbonates, iron oxides, mono-sulfides, certain 

chelated organic compounds 

3 Conc. hydrofluoric acid (HF; 48 percent) silicates 

4 2N (1:7) nitric acid (HNO3) disulfides, especially pyrite 

The trace-element distributions in the principal components of coal (i.e., ion exchange sites, 

carbonates, sulfates, silicates, sulfides, and chelating sites in the organic fraction), together with 

other supporting information (such as the mineralogical composition of the “ash” and 

assumptions regarding the adsorption characteristics of both inorganic and organic components), 

can be extrapolated to approximate their distributions in sedimentary rocks of shallow aquifers. 

Although such a calculation can only be of qualitative value, it is nevertheless a helpful guide for 

planning similar quantitative evaluations of aquifer host rocks. Furthermore, it can also serve as a 

check on geochemical models of trace element behavior in aquifer host rocks, which of necessity 

are based almost entirely on laboratory measurements involving idealized systems. 
 

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Table 3.3. Distribution (in %) of iron and selected trace elements in coal (from Kolker et al., 2002) 

 Coal Type 

Element Ohio 5/6/7 Wyodak North Dakota 

Fe 80 

10 

10 

Pyrite 

Fe-oxides1

Silicates 

10 

75 

10 

Pyrite 

Fe-oxides1

Silicates 

10 

65 

15 

Pyrite 

Fe-oxides1

Silicates 

As 60 

25 

5 

10 

Pyrite 

Arsenates 

Silicates 

Organic2

15 

40 

15 

30 

Pyrite 

Arsenates 

Silicates 

Organic 

15 

50 

15 

20 

Pyrite 

Arsenates 

Silicates 

organic 

Ba 5 

90 

5 

Barite 

Silicates 

Organic (5%) 

80 

5 

15 

Barite 

Silicates 

Organic (5%)2

85 

15 

Barite 

Silicates 

Cd 20 

35 

40 

Sphalerite 

Pyrite 

Organic (5%)2

70 

10 

15 

Sphalerite 

Pyrite 

organic 

- 

- 

- 

ND 

ND 

ND 

Hg 60 

20 

10 

Pyrite 

Organic 

Exchangeable 

60 

20 

20 

Pyrite 

Organic 

Exchangeable 

80 

10 

Pyrite 

Organic 

Pb 35 

40 

10 

15 

Galena 

Pyrite 

Silicates 

Organic 

70 

10 

20 

Galena 

Pyrite 

Silicates 

85 

5 

10 

Galena 

Pyrite 

Silicates 

Sb 20 

60 

15 

Silicates 

Organic 

Pyrite 

35 

45 

15 

Silicates 

Organic 

Pyrite 

40 

50 

10 

Silicates 

Organic 

Pyrite 

Se 50 

50 

Pyrite 

Organic 

20 

75 

Pyrite 

Organic (5%)2

25 

70 

Pyrite 

Organic (10%)2

U 20 

5 

70 

Silicates 

HCl Soluble3

Organic4

30 

50 

15 

Silicates 

HCl Soluble3

Organic (10%)2,4

45 

45 

15 

Silicates 

HCl Soluble3

Organic4

Zn 30 

30 

10 

30 

Sphalerite 

Pyrite 

Silicates 

Organic (5%)2

50 

25 

10 

15 

Sphalerite 

Pyrite 

Silicates 

Organic 

65 

5 

10 

20 

Sphalerite 

Pyrite 

Silicates 

Organic 

 

1

 May also include oxidized pyrite and mono-sulfides 

2

 Number in parentheses after Organic % indicates percent in exchange sites. 

3

 Species unknown, may be associated with organic chelates, and/or with zircon. 4

 Small amounts may be associated with zircon. 
 

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Despite the undoubted value of the results regarding the distributions of trace elements in coals, 

it should be noted that the sequential selective leaching procedure employed in the study 

introduces artifacts that could misrepresent the true distribution of trace elements in the unmined 

in situ coals. In particular, the low-temperature ashing of the coal, conducted at about 200°C, can 

lead to the oxidation of redox sensitive elements, such as the presence of As as arsenate indicated 

in Table 3.3. Even preparation of the coal samples, such as their comminution prior to selective 

leaching, can result in partial oxidation, if conducted under atmospheric air. 

Table 3.4. Molar ratio of trace metals (Me) in relation to As in pyrite compared to their molar abundance ratio in 

a bulk sample of Ohio 5/6/7 Coal. R2

 denotes correlation. 

Trace 

Element 

Molar Ratio 

(Me:As) in 

pyrite 

R2

 Molar 

Ratio 

(Me:As) in 

bulk coal 

Relative 

Enrichment 

Enrichment 

In Pyrite 

As 1.000 1.00 1.000 1.000 18.11

Cd 0.0014 0.71 0.003714 0.377 6.82 

Hg 0.0112 0.65 0.003777 2.97 53.8 

Pb 0.392 0.73 0.1849 2.12 38.4 

Sb 0.0471 0.63 0.6707 0.702 12.7 

Se 0.964 0.92 0.0949 10.2 185 

Zn 0.0441 0.13 1.352 0.0362 0.66 

1

 Ratio of As concentration in pyrite (321 ppm) to hat in coal (17.8 ppm) 

Table 3.5. Sequential selective leaching procedure to characterize the distribution of trace elements in marine 

sediments (after Huerta-Diaz and Morse, 1992) 

# Fraction Procedure Elements Extracted from: 

1 Reactive Digestion of a freeze-dried 

sediment sample for 16 hours with 

1M HCI. 

Monosulfides, amorphous and 

crystalline iron, and manganese 

oxyhydroxides, carbonates, and 

hydrous aluminosilicates 

2 Silicate Two consecutive leachings with 

10M HF for 1 hour and 16 hours, 

respectively, followed by addition 

of boric acid to redissolve 

precipitated fluorides. 

Clay minerals 

3 Pyrite Digestion of the silicate fraction 

residue with concentrated nitric 

acid for 2 hours. 

Pyrite and associated trace metals 
 

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Of the extensive suite of trace elements studied, five (As, Cd, Hg, Pb and Zn) are pertinent to the 

current study. The distributions of these elements and of Fe between the reactive and pyritic 

fractions (as defined in Table 3.5) are shown in Figure 3.2, which illustrates the depth profiles in 

organic-rich marsh sediments from Atchafalaya Bay, Mississippi. This sediment contained 

connate water with a lower salinity (≤5 ‰) than other sedimentary environments studied by the 

investigators, and is therefore considered to be more relevant to the present evaluation. 

Fig. 3.2. Concentrations (per gram of sediment) of selected trace elements and Fe in the pyritic and reactive 

fractions of organic-rich marsh sediments from Atchafalaya Bay with depth below the sediment-water 

interface (after Huerta-Diaz and Morse, 1992). Dashed lines are illustrative only, and included to 

emphasize trends in the partitioning of trace metals between the reactive and pyritic components. 
 

24 of 352

Of particular interest is the observed partitioning of the trace metals between the reactive and 

pyritic components as a function of the extent of pyritization. Huerta-Diaz and Morse (1992) 

examined their behavior in terms of two parameters, the degree of trace-metal pyritization 

(DTMP): 

(%) 100 Re

Pyrite Me DTMP

Pyrite Me active Me

− = × −+ −

 (3.1) 

and the degree of pyritization (DOP), defined as: 

(%) 100 Re

Pyrite Fe DOP

Pyrite Fe active Fe

− = × −+ −

 (3.2) 

When DTMP is plotted against DOP, a measure of the degree of preferential partitioning of each 

trace element in pyrite is obtained. This is shown in Figure 3.3 for samples taken from the 

organic-rich marsh sediments from Atchafalaya Bay, Mississippi. 

Fig. 3.3. The degree of trace metal pyritization (DTMP) in terms of the degree of pyritization (DOP) for As, Cd, 

Hg, Pb and Zn in organic-rich marsh sediments from Atchafalaya Bay (after Huerta-Diaz and Morse, 

1992). 
 

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In the following paragraphs, we make reference to the information taken from Kolker et al. 

(2002) and from Huerta-Diaz and Morse (1992) in discussions of the likely behavior of each 

selected hazardous inorganic constituent as it relates to solid solution substitution in pyrite or to 

the formation of discrete phases forming separately from co-existing pyrite. While reactive 

transport modeling in Section 4 is applied with a focus on Pb and As, we provide here a 

summary evaluation of the behavior of several other potentially relevant hazardous inorganic 

constituents. 

Arsenic. An extensive literature testifies to the presence of As substituting for S as As(-I) in 

authigenic, diagenetic, and hydrothermal pyrite (Blanchard et al., 2006; Reich et al., 2005). The 

abundance of As in relation to the quantity of sulfide (-II) sulfur in pyrite is generally small (As 

≤ 0.05), although As substitution in FeS2 at higher temperatures is more extensive. Reich and 

Becker (2006) cite concentrations as high as 19 wt% observed in natural pyrite and marcasite 

samples. These investigators, on the basis of theoretical calculations, show that metastable As 

substitution in pyrite up to As ~ 0.05 is thermodynamically favored over the corresponding 

substitution in marcasite, a less stable polymorph of pyrite. At higher mass fractions, a sulfurrich arsenopyrite (FeAsS), which possesses the marcasite structure, would coexist with the 

metastable arsenian pyrite. 

Saturation with respect to arsenopyrite in the presence of arsenian pyrite in typical lowtemperature shallow potable water aquifers is unlikely, since the abundance of As is normally 

insufficient (see Section 3.4.5.2, Figure 3.34a). Indeed, only one example has been cited in the 

literature (Utsunomiya et al., 2003). However, Reich and Becker (2006) show that at 300 K, the 

ultimate state of thermodynamic equilibrium would be a pure end-member pyrite coexisting with 

sulfur-rich arsenopyrite. This state is not normally achieved in nature. Prolonged annealing of 

arsenian pyrite leads instead to exsolution of arsenopyrite domains (Fleet et al., 1989; Simon et 

al., 1999a,b; Palenik et al., 2004) that remain isolated within the pyrite. At low solid-solution 

concentrations, i.e., As ~ 0.01, unmixing appears to be too sluggish to be observable in field 

specimens (Savage et al., 2000). Arsenic mobilization will therefore be sensitive only to 

environmental changes that induce pyrite dissolution, although such changes, when induced by 
 

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oxidizing conditions, could be accelerated by the presence of As in pyrite (Blanchard et al., 

2007). 

The As content in authigenic pyrite as a function of depth in brackish and marine sediments 

shows that almost all As is sequestered by the pyrite soon after burial (Huerta-Diaz and Morse, 

1992), as shown in Figures 3.2 and 3.3. The partitioning of As in coal also supports the belief 

that As substitutes in autogenously generated pyrite (Finkelman, 1981; Kolker et al., 2002), and 

that, at most, only about 15% resides in coexisting silicates in coal. A significant concentration 

of As is, however, associated with organic matter probably complexed with thiol functional 

groups (Table 3.3). In contrast, the small organic content of arenaceous aquifers, usually less 

than one percent, would preclude such material being a significant host for As under normal 

circumstances. Therefore, the contribution of As adsorption on organic material to its overall 

distribution in aquifer host rocks can be ignored for the purpose of reactive transport modeling 

described in Section 4. 

Barium. The chemistry of barium in groundwaters is relatively simple, because it does not 

undergo any change in oxidation state in reducing groundwaters, nor does it form any minerals 

with reduced species of As, S, Sb, and Se. It is present primarily as barite (BaSO4) in subbituminous and lignitic coals (Table 3.3). In bituminous coal, it is present dominantly in the 

silicate component. The concentration of Ba in the latter is about one order of magnitude less 

than in the other two coals. The reason for its presence in the silicate component suggests the 

depletion of sulfate through reduction to sulfide. In the absence of sulfate, it can substitute as 

Ba+2 –Al+3 for K+ - Si+4 in potash feldspars, and occupy exchange sites in clays, both occurrences 

of which have been noted by Finkelman (1981). In coal, Ba can also substitute for Ca in 

crandallite (CaAl3(PO4)2(OH)5) and can also form the carbonates alstonite (BaCa(CO3)2) and 

witherite (BaCO3) (Finkelman, 1981). Because few, if any, potable groundwaters are seriously 

deficient in SO4

-2, the primary candidate for Ba solubility control is barite, although witherite 

(BaCO3) and crandallite might also be options in rare sulfate-deficient environments. 

Cadmium. Studies of the mineralogy of non-ferrous ore deposits universally recognize that 

cadmium is sequestered as a solid solution in sphalerite (ZnFeS) (Piatak, 2004), where Cd 
 

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concentrations can reach 5 wt%. Cadmium for industrial purposes is recovered primarily during 

the roasting and acid treatment of sphalerite, the principal zinc ore. In coal, Cd reports in 

secondary sphalerite in concentrations up to 2-wt% (Gluskoter and Lindahl, 1973; Finkelman, 

1981). Indeed, Gluskoter and Lindahl (1973) noted that the Zn:Cd ratio in bulk coal samples was 

essentially the same as found in the contained sphalerite. Kolker et al. (2002) confirm the strong 

concentration of Cd in sphalerite, but they noted that significant fractions are also partitioned in 

pyrite and organic material (Table 3.3). This is partially corroborated by laser ablation ICP-MS 

(Inductively Coupled Plasma–Mass Spectroscopy) analyses of trace elements in pyrite from 

Ohio 5/6/7 coal presented in the same report, where Cd shows a modest enrichment over 

background concentrations (Table 3.4). However, Huerta-Diaz and Morse (1992) show that Cd is 

essentially excluded from precipitating pyrite in marine and brackish sediments, as illustrated in 

Figures 3.2 and 3.3. 

Experimental investigations by Framson and Leckie (1978) show that neither Fe substitution in 

greenockite, nor Cd substitution in FeS takes place to any significant extent at 25°C. This 

behavior is similar to that of Zn, as is noted further below under the discussion for zinc. Given 

the exclusion of both Cd and Zn from precipitating pyrite, it is reasonable to assume that the CdS 

component is likely to be incorporated in sphalerite in reducing groundwaters. Experiments by 

Bostick et al. (2000) also show that Cd+2 can oxidize pyrite to elemental sulfur and iron 

hydroxides with co-precipitation of CdS. This observation is consistent with the experimental 

findings and thermodynamic predictions by Framson and Leckie (1978) that no ferrous sulfide 

can precipitate before all excess Cd+2 has precipitated from solution, and that Cd would 

precipitate as CdS in sediments primarily through surface exchange with ferrous monosulfide 

substrates. Thus, it is possible that the association of Cd with pyrite in coal may have involved 

redox reactions, leading to physical incorporation of a discrete CdS phase as an impurity. Cd can 

also substitute for Pb in galena—e.g., see Bethke and Barton (1971)—but the extent of solid 

solution at 25°C, << 0.005 mol fraction, precludes significant sequestrative competition with 

sphalerite. 
 

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Both Framson and Leckie (1978) and Davies-Colley et al. (1985) infer that Cd precipitates in the 

form of essentially pure (unspecified) CdS, or greenockite, in shallow marine sediments where 

high HS- activities are generated through bacterial reduction of sulfate. The latter authors also 

found that Cd activity appeared to be controlled by a poorly ordered, more soluble form of CdS 

than greenockite. No reference is made to the possible co-precipitation of Cd with Zn in 

sphalerite. Further clarification regarding the association of Zn with Cd under authigenic and 

diagenetic conditions in the field will be required to clarify the relationship. 

For the purpose of this study, we assume that Cd is taken up primarily in solid solution in 

sphalerite. However, as is noted under the following section on selenium, it is possible that Cd 

could also precipitate as cadmoselite (CdSe) under conditions where sphalerite has been 

destabilized by a somewhat higher oxidation state, as suggested by the existence of cadmoselite 

and other ore minerals in calcite veins associated with altered (hematite-stained) amphibolites in 

Argentina (Paar et al., 2002). However, at the type locality, cadmoselite is reported to coexist 

with cadmian sphalerite, ferroselite and pyrite (Bur’yanova et al., 1957). The existence of 

ferroselite suggests an extremely rich selenium environment, and that the cadmoselite is 

stabilized for that reason rather than being due to the destabilization of sphalerite. Clearly, phase 

relations in Se-rich assemblages require further study. See also the discussion on zinc, below. 

Mercury. During authigenesis, precipitating pyrite sequesters Hg, as is illustrated by field 

observations of Huerta-Diaz and Morse (1992) involving authigenic pyrite precipitation in 

brackish water sediments (Figures 3.2 and 3.3), which shows a 50-fold enrichment over 

background. Similar observations have been made in studies of coal—e.g., Finkelman (1981), 

Kolker et al. (2002), Diel et al. (2004), Kolker et al. (2006)—where Hg can be enriched in pyrite 

50 or more times over background concentrations (Table 3.4). In coals atypically rich in Hg, 

samples have been found to contain isolated particles of cinnabar (Finkelman, 1981; Brownfield 

et al., 2005), or finely disseminated particles of tiemannite (HgSe) (Tewalt et al., 2001). 

Clausthalite (PbSe) also appears to act as a host for Hg in coal, as one sample observed contained 

0.54wt% Hg, representing a 10,000 fold enrichment over the background concentration in the 

coal (Hower and Robertson, 2003). Galena can also act as a host for Hg. Finally, Barbanson et al. 
 

29 of 352

(1985) and Martinez Garcia et al. (1991) describe a variety of sphalerite containing up to 7% 

substitution of zinc by mercury. This variety has been found in the mines of Argayon, Cotos, 

Rubios, Tresviso, and Hozarco in Spain. Electrochemical studies (e.g., Giannetti et al., 2006) and 

spectroscopic studies of Hg adsorption on the pyrite surface (e.g., Ehrhardt et al., 2000), using Xray photo electron spectroscopy (XPS), and Behra et al. (2001), using XPS and X-ray absorption 

spectroscopy (XAS)), both affirm that Hg is strongly adsorbed by functional groups on the pyrite 

surface, which under acid conditions were identified as ≡S-1-Hg-OH or ≡S-1-Hg-Cl. However, it 

is not clear whether the Hg+2 becomes incorporated as a solid solution in the pyrite or 

concentrates in isolated HgS domains. We take a provisional approach by assuming that Hg 

enters into solid solution in pyrite in a reducing aquifer, but that substitution in galena or 

sphalerite, or formation as discrete cinnabar or tiemannite minerals, might be plausible. It is 

possible that, like As substitution in pyrite, HgS substitution is metastable, and that long-term 

recrystallization of pyrite leads to its eventual expulsion from the pyrite lattice. Thus, 

conclusions arrived at through a preliminary literature review must remain tentative, pending 

further thermodynamic analysis in Section 3.4.5. 

Lead. Mineralogical studies of coal suggest that galena is the dominant mineral containing Pb as 

an essential component (Kolker et al., 2002). However, Finkelman (1981) found that over 50% 

of coals in the Appalachian Basin and 10% outside the Basin contained PbSe (clausthalite) rather 

than galena, although it was estimated that at most no more than 50% of the Pb could be present 

in that form. Kolker et al. (2002) show that Pb does not appear to report in pyrite in significant 

amounts despite the preponderance of this mineral, except perhaps in bituminous coals from 

Ohio (Table 3.3). In such coals, Pb is enriched in pyrite by a factor of nearly 40 (Table 3.4). 

However, Finkelman (1981) suggests that Pb enrichment in pyrite might be due to the presence 

of impurities such as galena, which may well have been incorporated in a manner similar to that 

of CdS though redox reactions on the pyrite surface. This supposition is reinforced by the strong 

correlation displayed between Pb and Sb in pyrite samples from Ohio 5/6/7 coal, where Sb:Pb 

~0.13 and R2

 =0.91 and in As bearing pyrite from the Roudny gold deposit of the Bohemian 

Massif (Zacharias et al., 2004), where Sb:Pb ~1.5 with a correlation coefficient of 0.93 in one 

generation of pyrite. The latter authors believe that the stoichiometric ratio of Pb to Sb reflects 
 

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the possible presence of “crypto-inclusions” of jamesonite (Pb4FeSb6S14). A similar but 

somewhat weaker correlation between Pb and Sb (R2

 = 0.66) is also observed in arsenian pyrite 

associated with gold mineralization in neoproterozoic sediments of the Saldania Belt in South 

Africa (Belcher et al., 2004). Huerta-Diaz and Morse (1992) show that Pb is almost always 

quantitatively excluded from authigenic pyrite, in conformity with the observed behavior of Cd 

under the same conditions (Figures 3.2 and 3.3). Thus, we can conclude that galena is the 

predominant host mineral for Pb except where excess selenium stabilizes clausthalite, and that 

any association of Pb with pyrite is probably due to the presence of foreign inclusions. 

Antimony. Information on the mode of occurrence of antimony is sparse. In principle, it would 

be expected that Sb would behave somewhat similarly to As. Gudmundite (FeSbS) is an 

isostructural analogue of arsenopyrite, but occurs infrequently in Sb-rich epigenetic 

mineralization (Williams-Jones and Normand, 1997). Limited field evidence also indicates that 

Sb does report in epigenetic arsenian pyrite, although an expected correlation between As and Sb 

is not always observed (Zacharias et al., 2004). This finding is consistent with the complete lack 

of a correlation between As and Sb concentrations in a diverse set of pyrite samples associated 

with coals in the Black Warrior Basin of Northwestern Alabama (Diel et al., 2004). In contrast, a 

correlation between As and Sb in nine samples of pyrite from Ohio 5/6/7 suggests a sympathetic 

association (R2

 = 0.63), although Sb uptake appears to be less efficient than would be predicted 

on the basis of the As/Sb ratio in the bulk coal (Table 3.4). A sympathetic correlation between 

As and Sb (R2

 = 0.70) is also observed in arsenian pyrite associated with gold mineralization in 

Neoproterozoic sediments of the Saldania Belt in South Africa (Belcher et al., 2004). The 

somewhat less efficient uptake of pyrite from Ohio 5/6/7 may be due to a larger anionic radius of 

Sb(-I), when substituting for S(-II), or because of the slightly higher stability of Sb(III) compared 

with As(III) in aqueous solution. The sometimes strong correlation between Pb and Sb in pyrite, 

noted under the discussion for Pb, above, further suggests that Sb solid solution in pyrite may be 

restricted, and that Sb is incorporated as nanoparticulate inclusions. The mode of occurrence of 

Sb in coals (Table 3.5) also indicates that significant fractions of Sb are retained by the organic 

and silicate components, in agreement with a review of earlier studies by Finkelman (1981). 
 

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Therefore, it appears that Sb chemistry differs sufficiently that a correlative chemical behavior 

with As in pyrite cannot be presumed. 

The most commonly observed Sb host mineral under reducing conditions is stibnite (Sb2S3). 

However, Williams-Jones and Normand (1997) point out that stibnite is destabilized in favor of 

kermesite (Sb2S2O) below 150oC. According to these authors, the vast majority of occurrences of 

kermesite are of supergene origin. There are several well-documented occurrences of kermesite 

having been formed through hypogene processes. In the event that pyrite is destabilized, any 

released Sb is therefore likely to precipitate as stibnite above 150oC and as kermesite below that 

temperature, because both possess stability fields extending to more oxidizing conditions than 

that of pyrite. Stibnite and kermesite also become more stable with decreasing pH and/or H2S 

activity (Williams-Jones and Normand, 1997). 

The recently discovered, rare antimony analogue of orpiment, antimonselite (Sb2Se3) (Chen et 

al., 1993), has been reported in epithermal deposits associated with galena, sphalerite, and pyrite, 

marcasite, uraninite, coffinite and organic matter (Min et al., 2000), and with tiemannite, 

clausthalite, and berzelianite (Cu2Se) (Vikre, 2005). Antimonselite has also been observed to 

form a solid solution series with stibnite (Liu et al., 2005). Because the antimonselite stability 

field also extends to significantly higher redox potentials than pyrite, its formation could result 

from the liberation of both Sb and Se upon oxidation of the latter. Accordingly, the presence of 

antimonselite in sedimentary rocks should not be discounted. 

In the absence of more definitive information, we tentatively conclude that Sb preferentially 

substitutes for S in the pyrite lattice in a manner similar to that of As, but that stibnite, kermesite, 

and antimonselite might also host Sb, especially under less reducing conditions where pyrite is 

no longer stable. 

Selenium. Selenium substitutes for sulfur in the pyrite lattice, and a limited solid solution is 

observed between pyrite and its selenium metastable isostructural analogue, dzharkenite (FeSe2) 

(Yakovleva et al., 2003). Limited field evidence from Western phosphatic shales (Ryser et al., 

2005) suggests that dzharkenite may form preferentially in the presence of excess Se under 
 

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diagenetic conditions. However, the more stable form of FeSe2 is ferroselite, which possesses the 

marcasite structure. An examination of the behavior of Se in relation to As in pyrite in Ohio 

bituminous coal shows a remarkable coherence between the two, as shown in Figure 3.4. A 

stoichiometric ratio close to unity is revealed, with R2

 = 0.92 (Table 3.4), despite the ten-fold 

greater abundance of As. Finkelman (1981) also noted a parallel behavior between As and Se in 

pyrite from Upper Freeport coal, although in another sample of the same coal, a negative 

correlation was observed. Whether the correlation in pyrite in coal from Ohio is fortuitous or 

reflects an underlying energetically favored coupled substitution is not known. However, the 

mode of occurrence of selenium in coals suggests that excess Se will preferentially co-precipitate 

with Pb as clausthalite (Finkelman, 1981) or mercury as tiemannite (Tewalt et al., 2001) before 

saturation with respect to either dzharkenite or ferroselite. Neither dzharkenite nor ferroselite 

have been observed in coal, an indication that the relative abundance of Se in coal is insufficient 

to saturate with respect to either of these phases. As noted at the conclusion of Section 3.4.5.1, 

the coexistence of selenian pyrite with dzharkenite in Western phosphatic shales (Ryser et al., 

2005) is probably unusual, since it occurs where the ambient concentration of Se is up to three 

orders of magnitude higher than the average crustal abundance. In the event that such an 

association is observed, Se should, depending on availability, preferentially co-precipitate heavy 

metal selenides such as tiemannite, clausthalite, and cadmoselite, in preference to cinnabar, 

galena and greenockite or cadmian sphalerite, which is generally consistent with reported field 

occurrences—e.g., Bur'yanova et al. (1957). 

Laboratory studies show an extensive solid solution series between ZnS and ZnSe at 

temperatures at least as low as 160°C (Wright et al., 1965). Because of the relative abundance of 

Zn in relation to Se in nature, it is not expected that sufficient excess Se would permit the 

stabilization of the selenium analogue of sphalerite, stilleite (ZnSe), except where more oxidizing 

conditions would cause the decomposition of sphalerite in favor of stilleite and zinc carbonates 

or silicates. Such a mechanism might also explain the occasional occurrence of the selenium 

analogue of greenockite, cadmoselite (CdSe), in sedimentary strata under reducing conditions 

despite the potential for Cd sequestration by sphalerite. The thermodynamic analysis (Section 

3.4.5.2) likewise suggests that the aqueous concentration of Cd could be controlled by 
 

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cadmoselite in preference to greenockite or hawleyite. Unfortunately, the general lack of 

thermodynamic data and information on the solid solution behavior of Se in FeS2 polymorphs 

and of Cd, Hg, Pb and Zn in FeS2 polymorphs in relation to their monosulfides and 

monoselenides precludes a quantitative assessment at this time. We will therefore assume that 

ferroselite (for which thermodynamic data are available) can be treated as a solid solution 

component in pyrite under reducing groundwater conditions, while monitoring the potential for 

saturation control by the Cd, Hg, and Pb monoselenides (cadmoselite, tiemannite, and 

clausthalite) respectively. 

Fig. 3.4. The correlation between the molar concentration of Se and As, determined by laser-ablation ICP-MS, 

in pyrite from Ohio 5/6/7 bituminous coal (from Kolker et al., 2002) 

Uranium. Extensive evidence indicates that U is mobilized in intermediate to acid igneous 

intrusive and particularly extrusive rocks, under oxic to mildly reducing conditions as the uranyl 

ion (UO2

+2). The source of UO2+2 is probably apatite, which is a known host for U in igneous 

rocks. However, uranium in sedimentary rocks is invariably associated with organic matter 
 

34 of 352

(Abdel-Gawad and Kerr, 1961; Szalay, 1964; Mohan et al., 1982; Drew Ilger et al., 1987) or with 

accessory detrital minerals such as zircon (ZrSiO4). Humic acids within the organic matter 

sequester UO2

+2 through complexation on carboxyl functional groups (R-COOH) (Szalay, 1964; 

Drew Ilger et al., 1987), primarily as monodentate complexes, i.e., 

R-COOH + UO2

+2 = R-COOUO2+

 + H+ 

or 

R-COOH + UO2

+2 + H2O = R-COOUO2OH + 2H+

(e.g., see Nefedov et al., 1998) leading to enrichment factors over contacting groundwaters by a 

factor as high as 10,000 (Szalay, 1964). Evidence suggests that a significant portion of the U 

remains adsorbed as metastable UO2

+2. However, field evidence from many sedimentary 

uranium deposits indicates that some adsorbed UO2

+2 is reduced from the (VI) to the (IV) state, 

through reduction by HS-, resulting from either biogenic or abiogenic activity (Min et al., 2000), 

e.g., 

4UO2

+2 + H2S + 4H2O = 4UO2 +SO4-2 +10H+

although direct microbial reduction of U(VI) to U(IV) has also been observed under anaerobic 

conditions (Suzuki et al., 2005). Autogenous radiolysis of water and/or organic matter—e.g., see 

Levinson (1977) and Dubessy et al. (1988)—and reduction by reactive hydrogen free radicals 

(H′) could also be operative, possibly with concomitant oxidation of excess organic material 

(Min et al., 2000), leading to the formation of uraninite and/or coffinite, thus: 

UO2

+2 + 4H ̊ = U+4+ 2H2O 

U+4 +2H2O = UO2(uraninite) +4H+

U+4 + SiO2(aq) + 2H2O =UO2(coffinite) +4H+

UO2 + SiO2(aq) = USiO4 (coffinite) 
 

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Both uraninite (mostly as pitchblende, a poorly ordered and partially oxidized form of uraninite) 

and coffinite have been reported to occur in association with lignitic, humic, and bitumic 

material in sedimentary rocks, and in coal beds in a few cases (Drew Ilger et al., 1987), despite 

significant concentrations of U. When found together, they appear to have been co-generated 

(Min et al., 2000). Lack of detection of either or both minerals may result from their occurrence 

in a colloidal or nanoparticular state (Min et al., 2000). 

U(IV) has also been reported occasionally in sedimentary rocks as brannerite (UTi2O6) 

(Finkelman and Klemic, 1976; Dutkiewicz et al., 2003), which probably forms through the 

interaction of dissolved TiO2 with uraninite, thus: 

UO2 + 2TiO2(aq) = UTi2O6 (brannerite) 

The possible presence of brannerite in potable water aquifers should not be discounted, 

particularly when thermodynamic data is available to calculate its solubility (Helean et al., 2003). 

However, the very low solubilities of potential sources of TiO2(aq), such as anatase, rutile or 

sphene, which are 3–4 orders of magnitude less soluble than the silica polymorphs, precludes 

rapid formation of this mineral, explaining its normal occurrence primarily in Paleozoic and 

proterozoic sediments (Finkelman and Klemic, 1976; Dutkiewicz et al., 2003). Uranium is also 

found in zircon, but because of its extremely low solubility and indefinite persistence in nature, 

any contained U is essentially isolated from groundwater, and therefore would be of no concern 

in the present study. 

The reported distribution of U in coal, as shown in Table 3.3, is consistent with the previously 

cited findings regarding its association with organic material. A significant fraction of the U is 

HCl-soluble, i.e., it is readily desorbed from carboxyl functional groups as UO2

+2, whereas the 

remainder is present in the silicate fraction (as coffinite?) and in the organic fraction, where its 

mode of occurrence is uncertain, but probably in the form of organic or mineral colloids. 

Finkelman (1981) has reviewed an extensive earlier literature on the occurrence of U in coal, and 

reaches essentially similar conclusions regarding its distribution. 
 

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We therefore assume that U is present primarily as uraninite, or as coffinite, in groundwaters 

under reducing conditions. 

Zinc. Both field and experimental evidence uniformly indicate that sphalerite (ZnS) is the 

predominant host for zinc under reducing conditions. Furthermore, Framson and Leckie (1978), 

both on experimental and theoretical grounds, argue that Zn is unlikely to substitute in iron 

sulfides, citing Barton and Toulmin (1966), who found that substitution of Zn+2 in pyrrhotite, 

even at temperatures as high as 850°C, is only 0.006 mol fraction. This argument is further 

substantiated by field observations. In the sedimentary environment, Zn partitions negligibly into 

authigenically formed pyrite, as shown in Figures 3.2 and 3.3 (Huerta-Diaz and Morse, 1992). 

Measurements of Zn concentrations in pyrite from Ohio 5/6/7 coal indicates that Zn is the only 

trace element that is depleted in pyrite compared with its concentration in bulk coal (Table 3.4). 

The partitioning of Zn into sphalerite dominates its behavior in coal (Table 3.3). Finkelman 

(1981), on the basis of a review of the earlier literature and his own mineralogical studies, also 

concluded that sphalerite is the primary mineral host for Zn. Thus, we can assume that sphalerite 

controls Zn solubility in groundwaters under reducing conditions. 

Summary

In Table 3.6, we summarize the expected occurrences of minerals, which might determine the 

activities of the selected hazardous inorganic constituents in reducing groundwaters. Where 

thermodynamic data are available, solubility products for these minerals have been incorporated 

in the EQ3/6 Data0.dat database, and in the corresponding database in TOUGHREACT, to 

permit calculation of potential thermodynamic controls of the selected hazardous constituents in 

groundwaters, respectively under static equilibrium and dynamic conditions. 

It should be emphasized that the chemical behavior of the selected hazardous inorganic 

constituents in groundwaters is affected by many processes, which include the past history of the 

aquifer, reactive chemical processes involving advective transport from distant sources, 

segregation due to changing host rocks, oxidation states and salinities, and the current oxidation 

state of the aquifer at the time of sampling. Because of the complexity of the chemical systems 
 

37 of 352

involved, the preceding review is intended as a preliminary survey, and is subject to future 

refinement. However, despite this cautionary note, it is believed that the selected suite of 

minerals presented in Table 3.6 provides a suitable starting point for geochemical modeling 

thermodynamic controls in Section 3.4.5. The thermodynamic properties of the identified 

minerals and associated aqueous species of the hazardous constituents (Appendices C, D, and E) 

were used in compiling solubility product and dissociation constant data, respectively, for 

incorporation in the EQ3/6 code. The code was then used in evaluating potable groundwater 

quality analyses to find out whether the identified minerals might be controlling the 

concentrations of the hazardous constituents in solution. This procedure is described in the next 

section. 

Table 3.6. Potential mineral hosts for hazardous inorganic constituents under reducing conditions 

Hazardous Potential Mineral Controls 

Inorganic 

Constituent Solid 

Solution 

Component 

Discrete Mineral 

As (FeAsS)py Arsenopyrite (FeAsS) 

Ba - Barite (BaSO4); Witherite (BaCO3); [Crandallite 

(CaAl3(PO4)2(OH)5)] 

Cd (CdS)sph Greenockite(CdS); Cadmoselite (CdSe) 

Hg (HgS)py Cinnabar (HgS); Tiemannite (HgSe) 

Pb - Galena (PbS); Clausthalite (PbSe) 

Sb (FeSbS)py Stibnite (Sb2S3); Kermesite (Sb2S2O); Antimonselite 

(Sb2Se3); Gudmundite (FeSbS) 

Se (FeSe2)py Ferroselite (FeSe2); [Dzharkenite (FeSe2)]; Antimonselite 

(Sb2Se3); Cadmoselite (CdSe); Clausthalite (PbSe); 

Tiemannite (HgSe) 

U - Uraninite (UO2); Coffinite (USiO4) [Brannerite (UTi2O6)] 

Zn - Sphalerite (ZnS); Hemimorphite (Zn4Si2O7(OH)2·H2O) 

Note: py = pyrite, sp = sphalerite 
 

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3.3 Evaluation of Potable Groundwater Quality in Relation to 

Groundwater Model Development 

3.3.1 Background 

This section describes the geochemical evaluation of a large number of water-quality analyses 

with measurable concentrations of hazardous inorganic constituents, with the main objective of 

identifying minerals containing specified hazardous constituents that saturate these waters. Many 

groundwaters that are recovered for use as drinking water in the United States are analyzed, not 

only for major dissolved chemical components, but also for trace concentrations of hazardous 

inorganic (and organic) constituents. Where comprehensive chemical analyses are available, the 

thermodynamic controls that govern the concentrations of the hazardous inorganic constituents 

in solution can be identified through comparison of the saturation indices of candidate minerals 

identified in the previous section. This is accomplished in this study through the use of the 

distribution-of-species code EQ3/6 (Wolery et al., 1993), which calculates the thermodynamic 

activities of all dissolved components. The thermodynamic activities of all dissolved components 

are then used to calculate the saturation indices of minerals that are potentially in equilibrium 

with the groundwater, including those where the hazardous constituent is an essential 

component. Other useful information is obtained from the thermodynamic evaluation (e.g., 

aquifer buffering capacity, redox potential, background levels of CO2 concentration), which, 

together with independent information relating to the mineralogical and physical properties of 

the aquifer host rocks, can be used to support development of a comprehensive geochemical 

model. 

The USGS National Water Information System (NWIS) Database contains approximately a 

quarter of a million chemical analyses of groundwaters of which a small fraction meets the needs 

of this project. Data mining of the NWIS was kindly conducted by Jonathon C. Scott, USGS, 

Oklahoma City, searching the database for all available data on well water specifically used for 

potable purposes. Search criteria were defined as follows: 
 

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1. The groundwaters had to be recovered from wells for drinking water purposes. 

2. The chemical analysis had to include at least one of the following elements: As, Ba, Cd, 

Hg, Pb, Sb, Se, U, Zn. 

3. The trace metal analyses had to be accompanied by complete chemical analyses of the 

water, i.e., pH, Na, K, Mg, Ca, Cl, SO4, CO3 or alkalinity, and if possible Fe(II), Fe(III) 

or Fe(T), Si or SiO2(aq), Al. 

4. Additional information was given that helped define oxidation state, e.g., Eh, dissolved 

oxygen concentration, or information that would help with the calculation of potential 

redox pairs such as CH4/CO2, H2S/H2SO4, As(III)/As(V), Sb(III)/Sb(V). 

5. Electrical conductivity was measured. 

In addition, the following supplementary data for each well site was extracted, when available: 

1. The geographic coordinate of the well site 

2. The geologic formation penetrated 

3. The well screen intervals 

4. The groundwater temperature 

5. Information pertaining to the mineralogy or chemical composition of well core or chips. 

Using the above search criteria, data files were extracted from the USGS National Water Quality 

Information System (NWIS) containing 38,610 water-quality analyses with 703,352 distinct 

measurements (total) at 22,437 unique data-collection sites nationwide. This large database was 

uploaded into a data management tool (based on the Matlab software) developed specifically for 

this study. The following sections describe the analyses conducted and discuss results. Section 

3.3.2 shows the concentration histograms of the major chemical constituents of the examined 

water quality analyses and certain other parameters of interest. Section 3.3.3 shows concentration 

histograms for the hazardous inorganic constituents selected for study, and illustrates spatial 

distributions for As, Ba, Pb and U. The raw data presented in Section 3.3.3 are subject to 

limitations arising from the analytical methods employed in the detection of trace concentrations 

of hazardous constituents. Discussion of applicable detection limits is given in Section 3.3.4, and 

the impact of analytical artifacts evident in some of the histograms is discussed in Section 3.3.5. 
 

40 of 352

3.3.2 Concentration of Major Constituents in Potable Groundwater 

The downloaded NWIS database of more than 38,000 analyses was first characterized in terms 

of the distributions of the major constituents contained in all groundwaters, i.e., Na, K, Mg, Ca, 

Fe(total), HCO3

-

, SO4, Cl and pH. In addition, analyses of Al, a minor species, with a typical 

concentration of less than 0.01 mg/L, were also compiled, because of its importance in 

geochemical modeling. SiO2(aq) was omitted from the dataset initially downloaded from the 

NWIS database, but was computed in the following step. The suite of chemical analyses was 

processed using the EQ3/6 code (see Section 3.4.5) in order to evaluate several additional species 

that are best represented in a form not readily obtainable from the analytical data, and which are 

required for comparison with the simulated groundwater composition generated by 

TOUGHREACT under initial conditions of secular equilibrium. The species included Fe(II), 

total HCO3

-

 and Eh. SiO2(aq) was set at saturation with respect to opal-CT, which is 

characteristic of many shallow groundwaters (Abercrombie et al., 1994; Apps, 2004). Total 

HCO3

-

 was calculated from the entered “free” HCO3

-

 or alkalinity, see Section 3.4.3 below. The 

Eh was imposed through an EQ3/6 input specification that both pyrite and goethite are at 

equilibrium at a given pH. In addition, the calculated concentration of Al was also retrieved from 

the output for comparison with the analyses of Al in the NWIS samples. The calculated value 

was based on the assumption that the groundwaters were also in equilibrium with a low Fe, Mg 

smectite. 

The resulting multiple sets of input data were stripped of analyses reporting ”greater than”, “less 

than”, “zero”, “presence verified but not quantified” concentrations, and the remainder, with the 

exception of pH, were converted to uniform concentration units of mg/L and transformed to a 

logarithmic scale. The remaining analyses for each constituent were divided into uniform bins, 

representing incremental concentration ranges on a logarithmic scale, and plotted as histograms. 

The output parameters from the EQ3/6 species distributions were similarly plotted as histograms. 

The results are given in Figures 3.5(a)–(m) below, and the appropriate modal value are 

summarized in Table 3.7. 
 

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Note that the analytical methods used for the quantification of major species in solution, with the 

exception of Al and Fe, are sufficiently sensitive to permit satisfactory measurement of their 

concentrations. The omission of unquantified analyses therefore has negligible bias on the 

results, particularly when it is recognized that it is the mode of the distribution that is of interest 

in this evaluation of the chemical analyses. This condition does not apply, however, to trace 

element concentrations at or near the limits of detection for the analytical method used, 

especially with regard to Al and Fe, and to most of the hazardous inorganic constituents 

identified for study in Section 3.3.3. For these constituents, the omission of unquantified 

analyses introduces a similarly unquantifiable positive skew to the plotted histograms. No 

techniques have been identified that would easily correct for this bias. The strategy adopted in 

this study is to independently develop a geochemical model, and compare the model with field 

observations as an aid to establishing the degree of bias and acceptability of the field data. It 

should be noted, however, that a positive bias can be inherently conservative, in that the bias 

could over-predict ambient concentrations of hazardous constituents in groundwaters in 

equilibrium with elevated partial pressures of CO2. 

Table 3.7 also includes additional parameters of interest for what is effectively the most 

commonly encountered or modal groundwater composition among those potable groundwater 

samples downloaded from the NWIS database. This modal groundwater is just slightly 

supersaturated with respect to calcite, and displays a relatively elevated P(CO2) (see Section 

3.4.3 for further discussion of P(CO2) in potable groundwaters). The composition represents of a 

groundwater of the Ca-Mg bicarbonate type. The “analytical” charge imbalance for this 

groundwater is excellent, being significantly less than the acceptable value of 5% of total charge. 

Note, however, that this composite charge imbalance is not representative of the average charge 

imbalance of the 38,000 groundwater analyses evaluated in this report. As is shown in Section 

3.4.3, about two-third of the studied groundwaters meet the 5% charge imbalance criterion for 

acceptability. The ionic strength is very low as expected, and is a useful benchmark for 

comparison with other aqueous systems. 
 

42 of 352

(a) (b) 

(c) (d) 

(e) (f) 

 Fig. 3.5. Part 1 
 

43 of 352

(g) (h) 

(i) (j) 

(k) (l) 

 Fig. 3.5. Part 2 
 

44 of 352

(m) 

Fig. 3.5. Histograms showing the concentration distributions of various chemical constituents, as well as pH and 

calculated Eh in more than 38,000 analyses of potable groundwaters in the United States. 

Table 3.7. Values of analytical concentrations of chemical constituents at maximum frequency and associated 

calculated values of selected species in more than 38,000 potable groundwaters. 

Parameter Unit Condition Value 

Na mg/L measured 12.6 

K mg/L measured 1.6 

Mg mg/L measured 20.0 

Ca mg/L measured 63.1 

Fe mg/L measured ≈1.3 

Fe(total) mg/L calculated 1.3 

Al mg/L measured ≈0.002 

Al(total) mg/L calculated 0.0002 

SiO2(aq) mg/L calculated 75.0 

Cl mg/L measured 15.8 

HCO3

-

 mg/L calculated 316 

SO4 mg/L measured 20.0 

pH Log (mol/L) measured 7.3 

Eh Volt calculated -0.18 

SI (calcite) - calculated +0.09 

P(CO2) Log (bar) calculated -1.86 

Ionic Strength Mol/kg H2O calculated 0.0078 

Charge Imbalance % of total charge calculated -1.75 
 

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The two constituents likely to show the greatest discrepancy between field observation and 

modeled values are Al and Fe (Figure 3.5i vs. 3.5j for Al, and Figure 3.5k vs. 3.5l for Fe). In the 

case of Al, only the more recently developed analytical method employing inductively coupled 

plasma with mass spectroscopy (ICP-MS) is sufficiently sensitive to allow quantitative 

measurements in groundwaters. Figure 3.5i shows the distribution of analytical concentrations 

for Al using the PLM43 method (Appendix A), which employs ICP-MS. The detection limit for 

Al varies when using this method, the most sensitive being 0.001 and 0.002 mg/L. Of all 1,213 

analyses reported, 696, or 57%, reported detectable values of Al. This implies that the most 

common Al concentration lies close to the detection limit at circa 0.002 mg/L. However, even 

when taking into account all potential Al complexing in groundwaters, especially with respect to 

silica complexes (Tagirov and Schott, 2001), the calculated concentration is still one order of 

magnitude less at ≈0.0002 mg/L. The reason for the discrepancy probably lies in the precipitation 

of a metastable amorphous hydrated Al-Si silicate caused by the dissolution of unstable detrital 

feldspars (e.g., see Paces, 1978), and which has not yet been incorporated in the geochemical 

model. 

Iron analyses show a very broad range of values in analyzed groundwaters as shown by Figure 

3.5k. This range reflects in part the potential variation in oxidation state of groundwaters in 

relation to the Fe(III)/Fe(II) couple, but also artifacts introduced by exposure of the groundwater 

sample to air with consequent oxidation and precipitation of Fe(III). As a consequence, the 

reported analytical concentrations of Fe(T) cannot be considered wholly trustworthy. As is 

discussed further below (Section 3.4), the evaluation of the concentration distributions of 

hazardous inorganic constituents strongly suggests that most groundwaters are reducing, which is 

consistent with redox control by the pyrite/goethite redox buffer, or by other buffers controlling 

the redox potential within a similar range of values. 
 

46 of 352

3.3.3 Concentrations of Hazardous Inorganic Constituents in Potable 

Groundwater 

The NWIS database was mined for chemical analyses of As, Ba, Cd, Hg, Pb, Sb, Se, U and Zn. 

Similar to the procedure employed for major constituents, the resulting multiple datasets 

processed as follows. Analyses reporting ”greater than,” “less than,” “zero,” “presence verified 

but not quantified” concentrations were deleted, while the remainder were converted to uniform 

concentration units of mg/L and transformed to a logarithmic scale. (Note: The mg/L unit 

roughly corresponds to 1,000 ppb for the conditions considered.) The remaining population of 

analyses for each element was divided into uniform bins, representing incremental concentration 

ranges on a logarithmic scale, and plotted as histograms. Note that the elimination of 

unquantified analyses potentially introduces a positive skew to the resulting histogram, as noted 

in Section 3.3.2, above, which can bear on the ultimate interpretation of the data in terms of the 

geochemical model developed in Chapter 3. This issue is discussed with respect to certain 

specific hazardous constituents under their respective sub-headings in Section 3.4.5. 

The resulting histograms are given in Figures 3.6(a)–(i). For comparison, the maximum 

contaminant level (MCL) permitted (U.S. Environmental Protection Agency, 2004) is also 

plotted, except for Zn. See Table 3.8 for a list of the hazardous elements and respective MCLs in 

drinking water. (If MCLs are exceeded in more than 10% of tap water samples, water providers 

need to take corrective actions.) It is evident from Figure 3.6 that, but for a few outliers, for the 

majority of groundwater samples in the database, most of the elements meet MCL requirements 

under current conditions. A significant minor percentage of groundwaters containing As and U 

exceed the MCL for those elements, as noted by Focazio et al. (2006). 
 

47 of 352

(a) Pb (b) As 

(c) Ba (d) Cd 

(e) Hg (f) Sb 

 Fig. 3.6 (Part 1) 
 

48 of 352

(g) Se (h) U 

(i) Zn 

Fig. 3.6. Histograms showing the concentration distributions of selected hazardous constituents in more than 

38,000 analyses of potable groundwaters in the United States. MCL signifies the maximum 

contaminant level for each constituent. SDWR stands for secondary drinking water regulation. 

The histograms reported here are plotted without regard to analytical method or accuracy. All 

histograms are characterized by isolated bins with anomalously high populations. These bins 

contain those analyses with “round number” concentrations, such as 0.001 mg/L or 0.002 mg/L, 

considered to be stemming from analytical artifacts, as discussed in Section 3.3.5. 
 

49 of 352

Table 3.8. Hazardous inorganic constituents with EPA-specified Maximum Contaminant Levels (MCLs) 

Contaminant MCL (in mg/L) MCL (in ppb) 

As, arsenic 0.01 10 

Ba, barium 2 2000 

Cd, cadmium 0.005 5 

Hg, mercury 0.002 2 

Pb, lead 0.015 15 

Sb, antimony 0.006 6 

Se, selenium 0.05 50 

U, uranium 0.015 15 

The majority of the histograms in Figure 3.6 show concentrations ranging over three to five 

orders of magnitude. Apart from the analytical artifacts (see Section 3.3.5), the large spread 

might be attributed to several causes: 

1. The element concentration in the aqueous phase may not be controlled by a mineral of 

which the element is an essential component, meaning a saturation state has not been 

reached. The aqueous concentration is instead determined by the element concentration in 

solid solution, on ion exchange sites, or on surface complexation sites, or any distribution 

between all three. 

2. Variations in the bulk chemistry of the system may affect either the total mass of the 

element in solution, or may impact mass action controls. 

3. Different minerals of which the element is an essential component control the 

concentration in solution. 

4. One or more of the components of solubility-controlling minerals may vary naturally 

over a large range of concentrations, due to, for example, variations in the oxidation state. 

5. Large ranges in pH or the presence of complexants may lead to large ranges in saturation 

concentration of the element in the aqueous phase. 

6. Some samples might be contaminated through some form of accidental anthropogenic 

intervention. 
 

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The large aqueous concentration ranges displayed by the selected trace elements highlights the 

need for a thermodynamic analysis of the data. By calculating the thermodynamic potentials, it is 

possible to establish which minerals may control the solubility of an element. If such calculations 

show that no mineral containing that element as an essential component controls solubility, then 

other minerals may have to be considered, i.e., those minerals which incorporate the element in 

solid solution, on exchange sites, or on surface complexation sites. The thermodynamic aspects 

of this problem are discussed further in Section 3.4.5. The lack of saturation control of a 

hazardous inorganic constituent by a pure mineral of fixed stoichiometry containing that 

constituent as an essential component could lead to variability in aqueous concentrations that 

reflects the bulk composition of the aquifer host rocks, as appears to be the case with respect to 

As under oxidizing conditions (Smedley and Kinniburgh, 2008). 

Every stable element in the periodic table, including the hazardous trace elements of interest in 

this study, possesses a natural crustal abundance (e.g., see Section 3.6, Table 3.17). Geological 

and geochemical processes can lead to concentration or depletion of certain elements, depending 

on their chemical properties and the extent and magnitude of the processes involved. No 

processes, whether natural or artificial, are 100 percent effective in separating and concentrating 

a given element. Therefore, residues of any element can always be found in all natural materials. 

As a consequence, all elements targeted for study in this report occur ubiquitously in soils, 

sediments, and aquifer rocks throughout the world. In the United States, metal concentrations in 

soils and surficial sediments vary greatly with region, and over ranges of concentration that 

approach four orders of magnitude. This is illustrated for Pb, As, Ba, and U in distribution maps 

taken from the USGS National Geochemical Survey database (USGS, 2008) (Figure 3.7). 
 

51 of 352

(a)

(b)

(c)

Fig. 3.7. (Part 1) 
 

52 of 352

(d)

Fig. 3.7. Spatial distribution of hazardous constituents concentrations in soils and surficial sediments of (a) Pb 

and (b) As (c) Ba and (d) U in the forty-eight contiguous states, from National Geochemical Survey 

Database (USGS, 2008). 

To check whether the aqueous concentrations might mirror the local natural surficial soil and 

sediment abundances of these same elements in the aquifer host rocks, they were plotted on a 

map of the 48 contiguous states, as illustrated in Figures 3.8. The respective aqueous and 

soil/sediment distributions for each element show a weak correlation, which can be a result of 

natural and anthrophogenic impacts. We should note that all groundwaters would exhibit small 

(mostly below MCLs), but non-zero concentrations of hazardous inorganic constituents if 

measured with sufficiently sensitive analytical techniques, as a corollary of the assertion that 

these constituents also occur ubiquitously in soils, sediments, and aquifer rocks throughout the 

United States and elsewhere in the world. Differences between the aqueous and soil/sediment 

distributions may be caused by sediment transport, climatic variations leading to differing 

degrees of constituent retention in soils, or localized anthropogenic modifications, such as the 

environmental dispersion of Pb from vehicular exhausts or battery recycling facilities, river and 

lake contamination with mercury from chemical plants producing sodium hydroxide, or the 

application of phosphate fertilizers or gypsum soil amendments containing elevated 

concentrations of uranium. In each case, elevated surficial concentrations of these elements, and 

others might be observed in the absence of subsurface aquifer contamination. Modification of 

aquifer chemistry due to surficial anthropogenic contamination would depend on many factors 

such as leaching and transport rates, recharge locations, subsurface hydrogeologic conditions, the 
 

53 of 352

chemical properties of the transported elements, and withdrawal rates from potable aquifers. 

Such complexities can be elucidated only through case-by-case examination of specific aquifers, 

and not through a generalized review. 

Close examination of Figures 3.7 and 3.8 reveals clearly that the weak correlation between 

aqueous and soil/sediment distributions is in part caused by inconsistencies in sampling density 

and use of analytical methods. The water quality analyses are sparsely distributed in many parts 

of the country, while differing analytical methods on solid and aqueous samples were 

inconsistently applied from state to state and regionally. For example, as pointed out in Appendix 

B1, the “hot spots” of measurable Pb concentrations in Figure 3.8(a) are clearly correlated to 

states or regions where large numbers of high-quality analyses (i.e., with low enough detection 

limits) have been conducted. In contrast, states or regions with no or only few detectable Pb 

concentrations in Figure 3.8(a) correspond to locations with either no measurements or analyses 

with insufficient detection limits. 

A better correlation appears evident between different hazardous constituents in the aqueous 

samples than between aqueous and solid samples for a given hazardous constituent. For example, 

evidence for a “hot spot” of elevated Pb concentrations in the states of Illinois and Iowa is 

duplicated for As and barium. The origin and occurrence of these observed anomalies has been 

well documented by Goldhaber et al. (2003). 

The distribution of U in Figure 3.8 appears to differ from Pb, As, and Ba. This may reflect the 

proximity of weathering igneous granitic rocks, which are commonly enriched in U. It is likely 

that the dissolved species is UO2

++, since minerals containing this cation are relatively soluble, 

thereby preventing saturation control. The regional variation in metal concentration may also 

reflect variations in aquifer chemistry rather than lack of saturation control. Only a 

thermodynamic analysis can help resolve this uncertainty, which points to the need of conducting 

a distribution-of-species analysis of the selected groundwaters (Section 3.4.5). 
 

54 of 352

 (a) 

(b)

 (c) 

Fig. 3.8. (Part 1) 
 

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 (d)

Fig. 3.8. Spatial distribution of samples with elevated aqueous concentration (Log mg/L) of (a) Pb, (b) As (c) 

Ba and (d) U in the forty-eight contiguous states. The plotted concentrations were drawn from more 

than 38,000 groundwater quality analyses downloaded from the National Water Information System 

Database (NWIS). 

3.3.4 Detection Limits of Hazardous Inorganic Constituents in Groundwater 

The NWIS database has been compiled over a number of years, during which time newer, more 

accurate, and more precise analytical methods have supplanted earlier methods. Several of the 

earlier analytical methods were insufficiently sensitive to provide quantitative measurements of 

the concentrations of several of the hazardous constituents under investigation in this report. 

Even the most sensitive methods currently in use are not necessarily sufficiently sensitive in all 

cases to provide quantitative measurements. Some idea of the limitations of the methods can be 

gleaned from the percentage of analyses that were successful in providing a quantitative 

measurement. This percentage also provides a measure of the concentration range expected for a 

given constituent. For example, if the percentage of measurable values is 50%, we can expect 

that the most frequent concentration of that constituent is at or near the detection limit. If the 

value is only 20%, then it can be expected that the most frequent concentration is significantly 

below the detection limit, although an approximate idea of its value might be estimated from 

extrapolation of the observed tail providing the upper-bound of the dispersion profile. 
 

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In Table 3.9, we have summarized the percentage of samples with measurable concentrations 

(above the detection limits) compared to all samples taken for the most sensitive analytical 

methods. For each analytical method, a range of detection limits arises, due to variations in 

sample quality, background contamination, and other factors that might influence analytical 

precision and accuracy. A complete tabulation of all analytical methods used, their various 

detection limits, and the number of analyses recording measurements below and above the limits 

of detection are given in Appendix A in Tables A1(a)–(i). The different analytical methods are 

referred to using code names, e.g., PLM43, which refers to metal analyses on unfiltered water 

conducted with ICP-MS, one of the more sensitive methods in routine use. Descriptions of the 

analytical methods are provided in Appendix A in Table A2. Table 3.9 suggests that measurable 

concentrations are frequently observed in the groundwater samples, ranging from as low as 17% 

of all samples analyzed using PLM47 for Cd to as high as 96%, 97% and 94% of samples 

analyzed for barium, uranium, and zinc, respectively. These findings are a valuable adjunct in 

interpreting the thermodynamic analysis of water quality data in Section 3.4. 

Table 3.9. Percentage of samples with measurable concentrations of hazardous inorganic constituents in 

groundwater samples analyzed with the most sensitive analytical method 

Contaminant Analytical 

Method 

Code* 

Reported Range of 

Detection Limits 

(mg/L) 

Percentage of Samples 

above Detection Limit 

(%) 

Arsenic As PLM10 

PLM40 

0.00012 

0.0002 – 0.0009 

92 

87 

Barium Ba PLM43 0.0002 – 0.001 96 

Cadmium Cd PLM43 

PLM47 

0.00004 – 0.001 

0.00004 – 0.001 

24 

17 

Mercury Hg CV013 0.0001 – 0.001 12 

Lead Pb PLM43 

PLM48 

0.0001 – 0.004 

0.00006 – 0.001 

49 

82 

Antimony Sb PLM43 0.00005 – 0.002 18 

Selenium Se PLM40 0.0003 – 0.002 58 

Uranium U FL006 

FL007 

0.0004 

0.00001 

69 

97 

Zinc Zn PLM43 0.0006 – 0.010 94 

* See Tables A1 and A2 in Appendix A. 
 

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3.3.5 Analytical Artifacts Affecting Measurements of Hazardous Inorganic 

Constituents in Groundwater 

The distributions of several of the histograms shown in Figure 3.6 reflect artifacts introduced by 

the differing accuracy and sensitivity of the analytical methods used, rather than to some 

underlying chemical control affecting groundwater composition. This issue appears to be most 

prominent in analyses for Cd, Hg, Sb and Se, as evident by the presence of isolated bins with 

anomalously high populations, although the effect is also noticeable in the case of As and Pb. 

The elements Ba, U, and Zn appear to be relatively unaffected, because most analytical methods 

used over time have been sufficiently sensitive to record quantitative values of concentration. 

As noted in the preceding section, the analytical methods used, their various detection limits, and 

the number of analyses recording measurements below and above the limits of detection, and 

descriptions of the analytical methods are given in Appendix A. The NWIS database provides 

the respective detection limits only for those samples with concentrations reported as “less than”; 

in all other cases, measurable concentrations are reported in the database without further 

mentioning of the specific detection limit of the analytical method. Using Pb as an example (see 

Table A1(e)), we have obtained almost 2,000 samples with Pb concentrations analyzed using 

method PLM43. About 1,000 of these are non-detects, with 700 reporting less than 0.001 mg/L 

(1 ppb) and 280 reporting less than 0.0001 mg/L (0.1 ppb), indicating that different ICP-MS 

techniques with different detection limits have been utilized. The other half of about 1,000 

samples reports measurable concentrations. The database, however, does not report the specific 

detection limits for these samples. In Appendix B2, we make a further attempt to evaluate the 

significance of analytical artifacts for the elements of interest. 

An example in which almost all analyses could be artifacts is illustrated by the distribution of Cd 

analyses. Inspection of Figure 3.6(d) shows that there are essentially two sets of measurements 

corresponding to log concentration ranges of -4.6 to -3.0 (log mg/L) and -3.0 to -1.0 (log mg/L), 

respectively. Each set of analyses starts with an initial high frequency, apparently corresponding 

to the detection limit for most of the analyses, which then falls off with increasing concentration 
 

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until a relatively low frequency is recorded approximately one log unit above the detection limit. 

The question arises whether any of these data, even those provided by the most sensitive 

methods, can be used to determine thermodynamic controls for Cd. Table A1(c) shows that 

cadmium has been measured with methods PLM43 or PLM47 in many cases, with detection 

limits as low as 0.00004 mg/L. In the former method, 24% of the analyses yielded detectable 

values, whereas the latter yielded 17%. These low levels of detection independently suggest that 

current analytical methods for Cd may be of marginal utility, and that identification of minerals 

controlling the solubility of cadmium from the evaluation of groundwater data could be 

problematic. 

We may compare these detection limits to the expected maximum concentration of cadmium in 

groundwater when in equilibrium with a solubility-controlling mineral. As discussed in Section 

3.4.5 below, geochemical calculations using the most current thermodynamic data for 

greenockite (CdS), which would yield the maximum Cd concentration in solution for a 

representative reducing potable groundwater, show that the predicted concentration should be 

about 2×10-4 mg/L in the presence of a pyrite/goethite redox couple (see Figure 3.48), or five 

times the detection limit of the most sensitive analytical methods currently in use. This suggests 

that the saturation concentration should be detectable, provided that the analytical artifacts 

described above do not interfere, and that the predicted concentration is correct. However, the 

uncertainty associated with the predicted concentration is about ± 0.5 log units. Furthermore, 

Cd+2 is likely to substitute as a minor component in either sphalerite (ZnS) or galena, because of 

its low absolute abundance in crustal rocks, and therefore its concentration in groundwaters 

could be significantly lower than the predicted saturation concentration of greenockite. 

Of particular concern is the adequacy of analytical methods for Pb and As, because the 

interpretation of thermodynamic controls governing these elements are at the center of the 

current project for modeling the impact of CO2 intrusion on ambient concentrations in potable 

groundwaters. For example, Figures 3.6(a) indicates that similar artifacts are operative for the 

less sensitive analytical methods measuring Pb, i.e., those with detection limits of 0.001 mg/L 

and above. The analytical method PLM43, however, for which a detection limit of 0.0001 mg/L 
 

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(or 0.1 ppb) is reported in a large number of cases (see Table A1(e)), seems to be of sufficient 

sensitivity to measure reliable values. 

In Figure 3.9, to check this assumption, we have plotted a histogram with measured 

concentrations using only method PLM43. For reference, the predicted equilibrium log 

concentration (mg/L) for Pb+2 is –4.0 (i.e., 0.0001 mg/L or 0.1 ppb), calculated assuming that 

galena coexists with pyrite and goethite in a typical reducing groundwater (Figure 3.44) (using 

the solubility product for galena, Ks, based on thermochemical measurements described in 

Appendix D in Table D1). This concentration is incrementally higher than the lowest detection 

limit of analytical method PLM48 (0.00006 mg/L), which appears to be situated close to a local 

maximum in the frequency of Pb concentration values. This would indicate that, indeed, the 

aqueous concentration of Pb in these samples is likely to be controlled by the presence of galena. 

This supposition is reinforced by the observation that of the two methods, in 49% of the analyses 

by PLM43, measurable values of Pb were detected with a detection limit of 0.0001 mg/L, 

whereas with PLM48, 82% yielded measurable values with a detection limit of 0.00006 mg/L. 

Unfortunately, reported analyses using the less sensitive method in PLM43 (with a detection 

limit of 0.001 mg/L) appear to display the same characteristics as those analytical methods for 

Cd and Hg, showing isolated bins with anomalously high populations. It would therefore be 

desirable to eliminate from further consideration those reported analyses using the less sensitive 

method. However, as discussed above, the detection limit for each analysis with measurable 

concentrations is not reported in the NWIS database, and therefore, the two methods cannot be 

clearly discriminated. 

In the case of As, a similar evaluation to that shown for cadmium or Pb cannot be conducted: as 

in the majority of potable groundwaters, the concentration of As in solution is determined in 

reducing groundwaters not by a mineral of fixed stoichiometry, where As is an essential 

component, but by solid solution substitution of the FeAsS (arsenopyrite) component in pyrite. 

Consequently, the concentration of As in solution can vary, depending on the local abundance of 

As in the aquifer host rock and an equilibrium state established between the arsenopyrite solid 

solution, adsorption sites on host rock mineral surfaces, and the aqueous phase. A 
 

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thermodynamic analysis conducted elsewhere in this report (Section 3.4) confirms that no solid 

phase where As is an essential component controls the concentration of As in the potable 

groundwaters evaluated in this report The frequency histogram for arsenopyrite saturation 

indices shows that SI values are negative with a maximum frequency at SI = -3 (see Figure 3.34). 

Furthermore, no other As minerals were identified that showed higher SI values than those of 

arsenopyrite. Thus, the evidence is quite convincing that the As concentration in solution is 

controlled by the binary (metastable) solid solution of the FeAsS component in pyrite in the 

majority of groundwaters. A comparison can be made between predicted concentrations and 

those actually measured by developing a thermodynamic model describing the FeS2-FeAsS 

binary solid solution, and calculating the metastable equilibrium As concentrations in typical 

groundwaters from that model. Such an analysis would also require determining the magnitude 

of the solid solution in pyrite samples from representative potable water aquifers. Absent the 

current feasibility of such an exercise, it is considered sufficient in this study to estimate the As 

activity in equilibrium with the arsenopyrite (FeAsS) solid solution component and use this value 

in subsequent equilibrium and reactive-transport modeling studies. 

Fig. 3.9. Frequency distribution of Pb analyses in groundwaters using the analytical method PLM43 (ICP-MS). 

For reference, the predicted equilibrium concentration for Pb+2 is 0.0001 mg/L (or -4 in log mg/L), 

calculated assuming that galena co-exists with pyrite and goethite in a typical reducing groundwater. 
 

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Below, we examine the ability of the most sensitive analytical methods to arrive at quantitative 

As determinations in the potable groundwaters evaluated in this study. The most sensitive 

analytical methods reported are PLM10 and PLM40, both of which utilize ICP-MS, and which 

have reported detection limits of 0.00012 and between 0.0002 and 0.0009 mg/L respectively 

(Appendix A, Table A1(a)). For the former analytical method, the percentage of detectable 

concentrations out of the total number of determinations is 92%, whereas for the latter, it is 87%, 

indicating that both methods are sufficiently sensitive for quantitative detection of As in the 

majority of potable waters studied. Figure 3.10 illustrates the concentration frequency histogram 

for As using these two analytical methods. It is evident that the maximum concentration 

frequency occurs at Log As =-2.5 (mg/L), or about 0.003 mg/L, which confirms that PLM10 and 

PLM40, in particular, are suitable analytical methods. 

In conclusion, it can be stated that for several of the inorganic hazardous constituents under 

investigation, past and even current analytical methods of measuring groundwater concentrations 

are insufficiently sensitive for the purpose. This is clearly the case for cadmium, possibly also for 

mercury, antimony, and selenium. Reported values close to the detection limit may be subject to 

spurious readings, the frequency of which appears to decline in a log-linear manner with 

increasing concentration (Appendix B2). An explanation for this behavior is at present not 

known, but may be dependent on the analytical method used. Eliminating the spurious values is 

not presently feasible, because there is no basis for separating spurious from real values. A 

partial solution is to eliminate from consideration all analyses obtained using those methods that, 

on the basis of geochemical considerations, are clearly out of range. This approach gives 

reasonable results in the cases of Pb and As, for example. Barium, uranium, and zinc are of less 

concern, as most analytical methods used over time have been sufficiently sensitive to record 

quantitative values of concentration. Further discussion on the geochemical evaluation of 

thermodynamic controls in relation to the plausibility of analytical measurements of certain 

hazardous constituents is given in Section 3.4. 
 

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Fig. 3.10. Frequency distribution of As analyses in groundwaters using the analytical methods PLM10 and 

PLM40 (ICP-MS). 

3.4 Thermodynamic Analysis of Potable Groundwater 

This section describes the identification of the thermodynamic controls that may influence the 

concentration and distribution of the selected hazardous inorganic constituents in the 

groundwater. We start in Section 3.4.1 with a description of the procedure for calculating the 

saturation indices of selected mineral hosts for hazardous constituents for the large number of 

groundwater quality analyses available from the NWIS database. Before this procedure can be 

applied, however, necessary modifications must be made to the thermodynamic database 

(Section 3.4.2), the integrity of the chemical analyses must be examined by evaluating charge 

imbalance (Section 3.4.3), and the groundwater redox state must be defined (Section 3.4.4). 

Results from the thermodynamic analysis are given in Section 3.4.5. Finally, the effect of redox 

state on the saturation concentration of Cd, Pb, and Zn is examined in Section 3.4.6. 

We selected the distribution-of-species code, EQ3/6 V.7.2b (Wolery et al., 1993), to calculate the 

saturation indices, as well as other parameters of interest such as ion activity ratios, which are 

related to the chemical potentials of components in the aqueous phase, Eh, charge balance, and 

the distribution of aqueous complexes in solution. This code has an associated database of 
 

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solubility products for solid phases and dissociation constants of aqueous species, data0.ymp.R4. 

The database was selected primarily because it had undergone several fairly recent revisions, 

especially with respect to the correction of errors in solubility products for silica polymorphs and 

rock-forming silicate minerals (Apps, 2004). Nevertheless, we realized that the database 

contained substantial amounts of obsolete and erroneous data, in some cases because data had 

been drawn from incompatible secondary data sources that were not checked for internal 

consistency. Extensive revisions were therefore undertaken in this study to rectify all relevant 

deficiencies, as further discussed in Section 3.4.2 below. 

To conduct thermodynamic analysis of a large number of groundwater data sets (more than 

38,000 water-quality analysis for a full set), we developed automated procedures for selecting 

and entering species concentrations in the input file of EQ3/6. The procedure involves entry of 

chemical analyses of each constituent in the EQ3 input file. When possible redundancies 

occurred, preferred analyses were chosen according to a hierarchical set of preferences. EQ3 was 

then executed, selected parameters were extracted and compiled, and the process was repeated 

for the next chemical analysis until all or a selected subset of samples was evaluated. If, for some 

reason, convergence was not obtained, the diagnostics were extracted and recorded for further 

review. The entire calculation was very rapid; i.e., the complete database of selected 

groundwater analyses from the National Water Information Database could be processed in a 

few hours. 

3.4.1 Calculation of Activities and Saturation Indices 

The saturation state is related to the Gibbs free energy, ΔGr, associated with the formation of the 

mineral, thus 

0

r ln ln a

G RT RT

a

Δ = = Ω (3.1) 
 

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where R is the gas constant ( units), a and a0 are, respectively, the mean molar activity of the 

participating species in the aqueous phase and the mean molar activity of the species in 

equilibrium with the mineral, and Ω is saturation state, given by: 

1

o

a IAP

a K

⎛ ⎞υ Ω= = ⎜ ⎟ ⎝ ⎠ (3.2) 

where IAP is the Ion Activity Product, K is the equilibrium constant, and υ is the number of 

atoms in the molecular unit. In this study, we follow the usual convention of defining the 

saturation index, SI, as: 

S I. . log = Ω (3.3) 

 

3.4.2 Modification and Augmentation of the Thermodynamic Data 

Supporting the Codes EQ3/6 and TOUGHREACT 

A critical requirement for meaningful interpretation and evaluation of hazardous-inorganicspecies behavior in groundwaters is a realistic thermodynamic description of the complete 

chemical system. The minimum thermochemical parameters needed for such a description 

include ΔGf ,Pr ,Tr

0 , SPr ,Tr0 , CPr0 and VPr ,Tr0 (these parameters are, respectively, the Gibbs free energy 

and entropy at the reference pressure and temperature (298.15 K, 1 bar), heat capacity at 1 bar as 

a function of temperature, and the molar volume at the reference temperature and pressure) for 

pure solid phases (i.e., minerals), the corresponding partial molal properties of dissolved species, 

i.e., ΔG f ,Pr ,Tr

0

, S Pr ,Tr

0

, CPr ,Tr

0

and V Pr ,Tr

0

, and an electrolyte model to describe the thermodynamic 

activities of the dissolved species in solution as a function of ionic strength. 

The distribution-of-species code, EQ3/6, used in this study for thermodynamic analyses, contains 

a database, Data0.dat, listing solubility products and dissociation constants for minerals and 

aqueous species. The version of the Data0.dat used in this study was qualified for use under the 
 

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U.S. Department of Energy’s Yucca Mountain Project, and is not fully suited for the demanding 

requirements of the current study. Much of the contained information is either irrelevant or has 

not been revised in the light of more recent data in the published literature. Data of critical 

importance to interpreting the behavior of the hazardous constituents selected for this study were 

never incorporated. We have therefore reviewed and, where necessary, revised or augmented the 

thermodynamic database. For consistency, we have conducted similar modifications to the 

database employed by TOUGHREACT, the reactive transport simulator utilized in Section 4. 

Because potable groundwaters are dilute and their ambient temperatures and pressures are 

relatively close to defined standard state conditions, a number of simplifications and 

approximations are possible in the derivation of thermodynamic data for this study. When 

necessary, simple corrections can be made to account for deviations in temperature or ionic 

strength. The methods used and approximations made in the relevant derivations are discussed at 

greater length in Appendix C. 

Augmentation of the thermodynamic data for solid phases consists of the following steps: 

1. Review the literature to find information on the thermodynamic parameters of solid 

phases of interest, based on calorimetric, thermochemical, phase equilibrium or solubility 

measurements, and crystallographic data. Where necessary, raw data must be processed 

to retrieve the needed parameters. 

2. Reconcile data to ensure that all thermodynamic data are internally consistent, i.e., are 

referenced to the same standards. 

3. Enter corrected data in the thermodynamic processing code, SUPCRT (Johnson et al., 

1992). 

4. Generate discrete log solubility product values, logKs, at 0, 25, 60, and 100°C, using 

SUPCRT. 

5. Process the SUPCRT output data in a format suitable for entry of solid phases in the 

Data0.dat database of EQ3/6. 

The processing of thermodynamic data for aqueous species is analogous. However, to maintain 

consistency with the SUPCRT format for aqueous species, parameters for the HKF (Helgeson-
 

66 of 352

Kirkham-Flowers) “Equation of State (EoS)” thermodynamic model (Tanger and Helgeson, 

1988) were also calculated, where feasible: 

1. Review of the literature to find information on the thermodynamic parameters of aqueous 

species of interest, based on calorimetric, thermochemical, or solubility measurements. 

Where necessary, reported dissociation constants are processed to retrieve the needed 

parameters. 

2. Reconcile data to ensure internal consistency, i.e., reference data to the same 

thermodynamic standards. 

3. Augment the basic data derived in Step 1 to calculate S, and the C1 and C2 parameters of 

the HKF EoS. The a1–a4 parameters of the HKF EoS are not needed for the purposes of 

this study. 

4. Enter corrected data in SUPCRT. 

5. Generate discrete log dissociation constant values, logKdiss, at 0, 25, 60, and 100°C, 

using SUPCRT. 

6. Process SUPCRT output data in a format suitable for entry in the Data0.dat aqueous 

species database of EQ3/6. 

The entire scope of the thermodynamic data enhancements is indicated in Table 3.10. 

It should be noted that the inclusion of certain minerals in the database is made with the 

understanding that the perturbation of aquifer systems—either resulting from the ingress of 

oxygenated groundwater, aggressive anaerobic bacterial reduction, or from the intrusion of CO2, 

or other anthropogenic perturbations—leads to the precipitation of metastable minerals that are 

finely crystalline or amorphous and which possess substantial specific surface areas. The large 

specific surface areas form important reactive adsorption sites that adsorb trace elements, 

including those hazardous constituents under study. In reactive transport modeling, the 

precipitation of metastable phases, and concurrent changes in specific surface area and 

adsorption capacity, can be accounted for using appropriate equations to describe the 

heterogeneous kinetics. 
 

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Table 3.10. Scope of modifications and augmentations to thermodynamic database 

Rock forming and accessory minerals

• Minerals participating in redox equilibria: 

− Ferric oxides and oxyhydroxides (ferrihydrite, goethite, lepidocrocite, hematite, ε−Fe2O3, 

maghemite) 

− Iron sulfides (pyrite, mackinawite, greigite, amorphous FeS) 

− Mixed oxide/sulfates (shwertmannite, green-rust) 

− Silicates containing Fe(II)/Fe(III), e.g. chlorite, smectite, illite

• Carbonates (dolomite, ankerite, siderite, magnesite)

• Silica polymorphs 

Minerals that contain a hazardous constituent as an essential component and are likely to 

be stable under moderate to strongly reducing groundwater conditions

• Arsenopyrite, arsenian pyrite, realgar, orpiment, symplesite (As) 

• Barite, witherite (Ba) 

• Greenockite, cadmoselite (Cd) 

• Cinnabar, tiemannite (Hg) 

• Galena, clausthalite (Pb) 

• Stibnite, kermesite, gudmundite, berthierite, antimonselite, (Sb) 

• Ferroselite, cadmoselite, tiemannite, clausthalite, antimonselite (Se) 

• Uraninite, coffinite, brannerite (U) 

• Sphalerite (Zn) 

Minerals that contain a hazardous constituent as an essential component and are likely to 

be stable under oxidizing groundwater conditions

• Scorodite, etc. (As). For a complete list, see Appendix C. 

• Barite, witherite (Ba) 

• Otavite, (Cd) 

• Quicksilver, calomel, etc. (Hg). See note. 

• Carbonates (cerussite, hydrocerussite, plumbonacrite, etc.) (Pb). For complete list, see 

Appendix C. 

• Senarmonite, valentinite (Sb). See note. 

• Other? (Se). See note 

• Uranophane, boltwoodite, weeksite, soddyite, etc. (U) 

• Hemimorphite, hydrozincite, smithsonite (Zn) 

Aqueous carbonate species for major rock-forming components, e.g., Mg, Ca, Fe(II).

Aqueous carbonate species for Ba, Cd, Hg, Pb, U(IV,VI), Zn

Arsenite (As(III)) and arsenate (As(V)) species and complexes, antimonite (Sb(III)) and 

antimonate (Sb(VI)) species and complexes; selenite (Se(IV)) and selenate (Se(VI)) species 

and complexes

Sulfide and selenide complexes of As, Cd, Hg, Pb, Sb, Se, Zn and Fe(II)

Note: Not all potential solubility-controlling minerals have been listed or identified. Some minerals may not have 

been recognized, or the thermodynamic properties have not been determined. Some minerals may substitute in solid 

solution as a minor component in a more abundant thermodynamically stable phase under the considered redox 

conditions. In some cases, thermodynamic data were estimated with varying degrees of precision and confidence, 

using various estimation techniques as described in Appendices C, D, and E. 
 

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A possible complication is the common nonattainment of thermodynamic equilibrium between 

species participating in redox reactions in the aqueous phase. This problem is well known, and 

has been investigated previously (Lindberg and Runnells, 1984; Stefansson, 2003). Problems of 

interpretation in the current study also extend to equilibria involving aqueous species of As (0, 

III, V); Hg(0, II); Sb (II, IV, VI); Se (II, IV, VI) and U (IV, VI). Furthermore, some of the 

species in solution form complexes with S(II) (which is also capable of several higher oxidation 

states, principally IV and VI) and with Fe(II) (which can also be oxidized to Fe(III)). Added 

complexity arises through the precipitation of phases with components in differing oxidation 

states, e.g., ferric arsenite (tooeleite), and ferrous arsenate (symplesite). Both EQ3/6 and 

TOUGHREACT can incorporate nonattainment of homogeneous equilibrium. In both codes, the 

respective reduced and oxidized forms can be treated as separate nonreacting species. In the case 

of TOUGHREACT, the kinetics of transformation from oxidized to reduced forms or vice versa 

can be included in the model. 

Finally, it should be recognized that hazardous constituents may co-precipitate to form minerals 

where two hazardous constituents are present as essential components. Examples are cadmoselite 

(SCdSe) or mimetite (Pb5(AsO4)3(Cl)). A priori evaluation regarding the ranges and pH and Eh 

conditions where these minerals might be stable is difficult, the easy way out being to 

incorporate the solubility products of all of these minerals in the thermodynamic database of any 

model of the groundwater system. 

Appendix C provides a detailed discussion of the thermodynamic data for both solid phases and 

aqueous species that were compiled for this study. The calculations performed to generate both 

the associated thermodynamic data and the relevant solubility products and dissociation 

constants are explained. In the discussion of results, it is noted where results are provisional, and 

how the inclusion of revised or otherwise missing data might impact the current findings. 
 

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3.4.3 Evaluating the Integrity of Water Quality Analyses 

One of the first tasks in evaluating the groundwater data using EQ3/6 is to evaluate the integrity 

of the chemical analyses. A rule of thumb for testing the quality of a chemical analysis is to 

check the charge balance according to the following equation: 

100

cations anions

cations anions

− ×

+

∑ ∑

∑ ∑ (3.4) 

The analysis is normally considered acceptable if the charge imbalance is ≤5%. However, the 

EQ3/6 evaluation demonstrates that imbalances were quite substantial for many of the chemical 

analyses extracted from the NWIS database. The primary reason is probably the application of 

analytical data that fail to take into account the effects of CO2 degassing and possible 

precipitation of calcium carbonate resulting from inadequate custodial preservation of sample 

integrity following field sampling and analysis, prior to laboratory determination of alkalinity. 

Most groundwaters are very dilute, so that the bicarbonate concentration contributes significantly 

to the charge balance. The bicarbonate concentration is related to the partial pressure of carbon 

dioxide as follows: 

 HCO3

-

 = CO2(g) + OH-  2

3

P OH CO

K

HCO

−

−

⎡ ⎤⎡ ⎤ ⎣ ⎦⎣ ⎦ = ⎡ ⎤ ⎣ ⎦

 (3.5) 

where the brackets [ ] represent activities of the enclosed species. When the equilibrium partial 

pressure of CO2 is calculated from chemical analyses of most groundwaters whose integrity is 

assured, it is found that it is significantly higher than that found in the atmosphere, i.e., Log 

P(CO2) varies between –3 and –1 compared with –3.5 for atmospheric conditions (see Figure 

3.17, below). Groundwater samples exposed to the atmosphere will therefore tend to degas, 

which usually leads to a corresponding upward shift in pH, supersaturation with respect to 

calcium carbonate, and eventual precipitation of calcium carbonate. This can be expressed by the 

following reaction: 
 

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2HCO3

-

 + Ca+2 = CO2(g) + CaCO3(s) + H2O 

If the pH of a sample is measured in the field before degassing occurs, whereas the water is 

analyzed for bicarbonate content or alkalinity after degassing, a significant charge imbalance can 

be expected when calculating the distribution of species in the sample using the reported values 

for pH and carbonate content. Charge imbalance can furthermore be caused if the resulting 

supersaturation induces precipitation of calcium carbonate prior to measurement of alkalinity. 

Less significant charge imbalances may also result from failure to account for the temperature 

difference between the groundwater and that at which analyses are conducted. 

The possible errors introduced as a result of unaccounted-for degassing and precipitation are 

illustrated in Table 3.11 for a typical groundwater containing a low concentration of dissolved 

solids, where it is assumed that the groundwater is in equilibrium with respect to calcite before 

sampling and analysis. In this table, seven changed states of the original sample, given in 

Column 3, are considered, together with certain assumptions made in interpreting the chemical 

analyses and their impact on calculated parameters. The “true” groundwater composition is 

described in Column 3 in terms of the measured analytical parameters (pH, HCO3

-

 [alkalinity] 

and Ca) and the calculated parameters (calcite saturation index SI[calcite), Log P(CO2) and 

charge imbalance), all listed in Column 1. The changed states are summarized in Columns 4–10, 

and are described as follows: 

• Column 4. The groundwater is allowed to warm up to normal room conditions, i.e., T = 25oC, 

resulting in a minor lowering of pH and slight supersaturation with respect to calcite. 

• Column 5. The groundwater is exposed to the atmosphere at 25oC and allowed to degas CO2

until Log P(CO2) = -3.5 (that of the atmosphere). The pH changes by approximately one unit, 

HCO3

-

 falls, and the sample becomes significantly supersaturated with respect to calcite. 

• Column 6. The degassed groundwater in Column 5 is allowed to re-equilibrate through 

precipitation of calcite. Both HCO3

-

 and Ca fall substantially, and SI(calcite) returns to zero. 

• Column 7. The effect on the calculated parameters is shown by assuming a pH measured at 

25oC in the field (Column 4) and a laboratory alkalinity determination after CO2 degassing 
 

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(Column 5). SI(calcite) increases slightly, Log P(CO2) is higher than that in the formation 

(Column 3), and the charge imbalance rises to +6% of the total, a value that would, according 

to standard criteria, make the overall analysis marginally unacceptable. 

• Column 8. The effect of accepting a field alkalinity determination and a laboratory pH after 

degassing CO2 is shown. SI(calcite) is strongly supersaturated, and Log P(CO2) is nearly one 

log unit lower than the true value in the formation, even though the charge imbalance 

remains relatively small. 

• Column 9. The field pH measurement is accepted together with an alkalinity determination 

after calcite precipitation, but assuming that Ca was measured on an aliquot acidified in the 

field prior to sample degassing and precipitation of calcite, as is standard practice. This leads 

to a relatively modest apparent undersaturation with respect to calcite and slightly lower 

P(CO2), but a drastic and normally unacceptable charge imbalance of +27%. 

• Column 10. The field pH measurement is accepted together with the alkalinity determination 

after CO2 degassing. SI(calcite) and Log P(CO2) are moderately affected, compared with the 

true values in the field, and charge imbalance increases by about +6%. 

Table 3.11. Errors introduced through inadequate preservation of sample integrity deficiencies. 

Parameter Unit Ground 

water 

with 

calcite at 

10°C 

Ground 

water 

raised 

to 25°C 

Degassed 

to P(CO2)

(atm) 

Degassed 

to P(CO2)

(atm) & 

calcite 

pptn 

Field 

pH; lab 

HCO3- 

Lab pH; 

degassed 

to P(CO2)

atm. Fld 

HCO3

-

Field 

pH Lab 

Ca 

After 

calcite 

pptn 

Field 

pH 

HCO3

-

after 

degas. 

pH - 7.739 7.611 8.694 8.548 7.611 8.694 7.611 7.611 

HCO3

-

 mg/L 227.29 227.29 197.06 114.81 197.81 227.29 114.81 197.06 

Ca++ mg/L 37.669 37.669 37.669 6.394 37.669 37.699 37.669 37.669 

SI (calcite) - 0.000 0.093 +1.132 0.000 +0.038 +1.130 -0.184 +0.036 

Log P(CO2) Bar -2.5 -2.286 -3.5 -3.5 -2.346 -3.372 -2.581 -2.348 

Charge 

imbalance 

% of 

total 

0.000 0.000 0.000 0.000 +6.00 -5.73 +27.4 +6.16 

Other constituents (mg/L): Na+, 45.8; K+, 5.04; Mg++, 2.01; Cl-, 7.14; SO4

-2, 18.40; SiO2(aq), 60.97. 
 

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A complication arising from low concentrations of dissolved solids typical of potable 

groundwaters is that the pH may be poorly buffered and subject to effects of contamination and 

instrumental drift. Thus, both pH measurements and alkalinity (or bicarbonate or carbonate) 

analyses may be subject to error. Unfortunately, the two parameters are interdependent, and 

therefore it is not possible to discriminate directly whether they may be erroneous. 

Analytical chemists are aware of the fugitive nature of CO2 and the tendency of pH to drift with 

time. Therefore, efforts are often made to measure both pH and alkalinity in the field, with 

additional backup analyses of these parameters (and/or bicarbonate or carbonate) in the 

laboratory. Because environmental conditions in the field are less well controlled than in the 

laboratory, field measurements can be less accurate, though more representative of actual 

conditions prior to sampling. Chemical analyses of groundwaters stored in the NWIS database 

commonly report both field and laboratory measurements for these parameters, sometimes 

additionally on filtered or unfiltered samples. Therefore it is necessary to choose those 

measurements of pH, alkalinity, or bicarbonate/carbonate that are most likely to provide accurate 

results during subsequent EQ3/6 calculations. 

The problem of selecting an appropriate value for alkalinity is further complicated by the 

multiplicity of different analytical methods used to measure alkalinity, bicarbonate, or total 

carbonate concentrations over the last decades. The NWIS database contains no less than 20 

listed analytical procedures for alkalinity, 12 for HCO3

-

 and 17 for CO3

-2. Their persistence in the 

NWIS database has a long history. Over time, USGS and EPA have attempted to standardize 

procedures and have supplanted earlier methods with improved techniques (Alexander et al., 

1988). However, the legacy of earlier techniques remains embedded in the NWIS database. To 

guide the choice of analytical methods, we developed a hierarchical 32-step protocol for the 

selection of HCO3

-

 (and CO3

-2) or alkalinity data from the database. All data was transformed to 

equivalent HCO3

-

 (mg/L) for entry in the EQ3 input file. 

Earlier versions of the EQ3 code (Wolery, 1983) possessed the option of entering the titration 

alkalinity directly as an entry in the input file, but this option was subsequently discontinued 

(Wolery, 1992, p. 21-27). The proposed solution advocated by Wolery (1993) involved the 
 

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calculation of the equilibrium concentration of CO2(aq) in the presence of HCO3

-

, then summing 

the molar concentration of CO2(aq) and the incremental titration equivalents of HCO3

-

 and CO3

-2, 

respectively for each analysis, and finally entering the appropriate total concentration of HCO3

-

for each analysis. This solution, however, would have been excessively time consuming. 

Instead, we opted to transform the measured alkalinity, or titration concentration of HCO3

-

, to 

HCO3

-

 (mg/L), and enter it in the input file as “free” HCO3

-

, i.e., the concentration of that 

specific species, regardless of the concentrations of other complexes of the HCO3

-

 basis species 

in solution. This automatically accounted for CO2(aq) and CO3

2- in the final species distribution, 

but also double counted the concentrations of aqueous complexes with HCO3

-

, such as 

NaHCO3(aq) or CaHCO3

+

. Because the latter complexes represent only a small fraction of total 

HCO3

-

, the error introduced by this approximation is generally small. 

We performed EQ3/6 runs on all 38,597 samples to evaluate the significance of sample 

degassing. As an additional constraint, we specified that Eh is determined by the coexistence of 

pyrite and goethite, a reasonable assumption for most anoxic groundwaters. (This assumption is 

evaluated further in the Section 3.4.5). Out of the total number of samples, 35,069 runs 

converged, and the initial pH, charge imbalance, P(CO2) and P(H2S), as well as calcite, dolomite 

and siderite (ferrous carbonate) saturation indices, were recorded. The resulting outputs, plotted 

in the form of histograms of the frequency of occurrence, are of considerable interest, not just for 

evaluating concerns about charge imbalance, but also with respect to characterizing the average 

geochemical conditions of most groundwaters. 

Figure 3.11 shows the pH range of all groundwaters. The acid “tail” is surprisingly persistent, 

and may reflect shallow groundwater contamination with organic acids, or relatively high CO2

partial pressures. The charge imbalance (Fig. 3.12) peaks at a slightly negative value, which 

would be expected if aqueous complexes of HCO3

-

 had been overcounted in the approximation 

of total HCO3

-

 concentration. The positive skew to the histogram has not been investigated, and 

remains to be explained. 
 

74 of 352

Fig. 3.11. Histogram showing the pH distribution of more than 35,000 groundwater analyses selected from the 

NWIS Database. No correction for charge imbalance. 

Fig. 3.12. Histogram showing the distribution of percentage charge imbalance of more than 35,000 groundwater 

analyses selected from the NWIS Database. 

The frequency histogram for the calcite saturation index (Fig. 3.13) displays a peak where SI is 

slightly above zero, indicating supersaturation with respect to calcite. The substantial population 
 

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of values approximately centered on SI ~ 0 suggests that the majority of groundwaters are 

saturated with respect to calcite. The cause of the secondary maximum at SI ~ -3 was not 

investigated. The pH of a groundwater saturated with respect to calcite is likely to be more 

strongly buffered and therefore be more resistant to change under increasing partial pressures of 

CO2. As a consequence, the concentrations of those hazardous inorganic constituents that are in 

part determined by ambient pH would be less impacted than would be the case where calcite is 

absent. It should be noted, however, that if calcite is present only in trace amounts, the ingress of 

high-pressure CO2 could lead to the complete dissolution of calcite, and the system would no 

longer be buffered by its presence. 

Fig. 3.13. Histogram showing the calculated calcite saturation indices of more than 35,000 groundwater analyses 

selected from the NWIS Database. No correction for charge imbalance. 

Many dolomitic limestones are sources of drinking water in the eastern United States, and it is 

therefore of interest to examine the state of saturation of all groundwaters with respect to this 

carbonate. Figure 3.14 shows that almost SI(dolomite) peaks at significantly less than zero, 

which is consistent with reported occurrences of dedolomitization in shallow groundwaters 

(Edmunds et al., 1982; Back et al., 1983; Plummer et al., 1990; James et al., 1993; Aravena et al., 
 

76 of 352

1995; sacks and Tihansky, 1996; Hopkins and Putnam, 2000; Smedley and Edmunds, 2002; 

Pacheco and Szocs, 2006). Therefore, it is to be expected that groundwaters will not come into 

thermodynamic equilibrium with those representative host-rock mineralogies containing 

dolomite, i.e., St Peter Sandstone and Dolomitic Limestone (Section 3.6.3) in TOUGHREACT 

simulations described in Section 4 of this report, unless complete dedolomitization occurs during 

the groundwater initialization step. 

Fig. 3.14. Histogram showing the calculated dolomite saturation indices of more than 35,000 groundwater 

analyses selected from the NWIS Database. No correction for charge imbalance. 

Because several authors cited above have attributed dedolomitization to concomitant dissolution 

of gypsum (CaSO4.2H2O) or anhydrite (CaSO4), it is worth checking to establish whether the 

selected water quality analyses are saturated with respect to calcium sulfate in one form or 

another. Figure 3.15 illustrates the SI distribution for gypsum. It is clear that few, if any potable 

groundwaters are saturated with respect to this mineral, and therefore buffering of the sulfate 

concentration or any influence it might have in defining the redox state via the SO4

-2/HS-

 buffer 

is negligible. Instead, it is more likely that barite may indirectly control SO4

-2 activity (Section 

3.4.4). It should also be noted that in the calculated equilibrium distribution of species using 
 

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EQ3/6, the partial pressure of H2S is defined though its relation to the activity of SO4

-2 in 

solution, thus: 

H2S(g) + 2O2(g) = 2H+

 + SO4

-2

where O2(g) and H+

 are respectively set by the specified by Eh and pH. See further discussion 

regarding P(H2S) below. 

Fig. 3.15. Histogram showing the calculated gypsum saturation indices of more than 35,000 groundwater 

analyses selected from the NWIS Database. No correction for charge imbalance. 

Figure 3.16 indicates that most groundwaters are not saturated with respect to the ferrous 

carbonate, siderite, which is consistent with relatively infrequent observations of its occurrence 

in shallow groundwater aquifers. Relaxation of the control of redox state through the coexistence of goethite and pyrite would likely decrease the saturation state even further with 

respect to siderite saturation. The finding that siderite may not be a common constituent of 

potable groundwater aquifers has three implications regarding the modeling of hazardous 

inorganic constituent behavior in potable aquifers being invaded by high-pressure CO2. First, its 

inclusion in the model mineralogy of the aquifer host rocks is not critical; second, it need not be 
 

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considered as a potential pH buffer; and third, consideration of a system whose redox state is 

buffered by a siderite/pyrite assemblage can be ignored in modeling general cases. 

Fig. 3.16. Histogram showing the distribution of calculated siderite saturation indices of more than 35,000 

groundwater analyses selected from the NWIS Database. No correction for charge imbalance. 

The distribution of partial pressures of CO2 in Figure 3.17 shows that, in a large majority of 

groundwaters, they are up to 2.5 orders of magnitude higher than those of the atmosphere (Log 

P(CO2) = -3.5), clearly indicating the potential of these groundwaters to degas if exposed to air. 

Therefore, it is preferable to assume that Log P(CO2) is buffered at circa –2.0 (the approximate 

modal value in the more than 35,000 groundwater samples examined), rather than imposing an 

external Log P(CO2) = -3.5, as was done by Wang and Jaffe (2004) in their preliminary study of 

the effect of high-pressure CO2 intrusion. 

Elevated P(CO2) in the subsurface environment is commonly attributed to the effects of 

microbial oxidation of organic matter. Indeed, this is most probably the case in shallow 

subaqueous unconsolidated sediments undergoing active authigenesis. However, elevated 

P(CO2) can also result from thermogenesis of organic matter and decarboxylation, or from 

deeper metasomatic processes, such as translocation of CO2 to shallower environments occurring 
 

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through diffusion/and or advective transport. Furthermore, we conducted test calculations with 

EQ3 of groundwater saturated with respect to representative diagenetic mineral assemblages 

including pyrite and goethite, calcite and siderite at 25°C, yielding P(CO2) levels comparable to 

those observed without recourse to the external introduction of biogenic CO2. Therefore it 

remains an open question whether biogenic CO2 modifies the diagenetic mineral assemblage, or 

whether the diagenetic assemblage specifies a P(CO2) that is elevated with respect to that of the 

atmosphere, regardless of the presence of organic matter. 

Fig. 3.17. Histogram showing the distribution of calculated Log P(CO2) of more than 35,000 groundwater 

analyses selected from the NWIS Database. No correction for charge imbalance. 

The P(H2S) distribution (partial pressure of H2S) in Figure 3.18 is fictive, in that it is the result of 

assuming that the coexisting pair of minerals, pyrite and goethite, defines the redox state at a 

given pH for a given SO4

-2 concentration. In reality, the distribution is primarily a reflection of 

the distribution of SO4

-2 in the population of groundwater analyses. However, the calculated 

range of concentrations is consistent with the fact that only in rare cases would H2S be detectable 

in solution, and that most anoxic groundwaters would be free of odor. P(H2S) is a measure of the 

hydrogen sulfide activity in solution, and has an important bearing on the extent of sulfide 

complexing of heavy metals in solution, including Cd, Hg, Pb, and Zn, and under some 
 

80 of 352

circumstances can also affect the behavior of As and Sb. It also defines the activities of these 

metals with respect to the solubility products of their sulfides. Thus this parameter is an 

important dependent variable in describing system behavior, just as are pH, Eh and P(CO2). 

Fig. 3.18. Histogram showing the distribution of calculated Log P(H2S) distribution of more than 35,000 

groundwater analyses selected from the NWIS Database. No correction for charge imbalance. 

In recognizing that the above results may in part be distorted by the potential loss of CO2, we 

conducted an alternative set of EQ3 runs with adjustment of the concentration of HCO3

-

 to 

eliminate charge imbalance. Figures 3.19 through 3.21 correspond to Figures 3.13, 3.16 and 

3.17, but include this correction. Examination of Figure 3.19 shows that by retaining a fixed pH, 

the saturation index for calcite increases, thereby shifting the SI calcite to higher values. This 

effect varies with pH, but in aggregate, it leads to a compression of the range and an upscale shift 

of both primary and secondary maxima, the secondary maximum being more strongly affected. 

The histogram for siderite saturation indices (Fig. 3.20) transforms into a nearly symmetrical 

peak about SI = 0. This result might lead to the conclusion that most groundwaters are saturated 

with respect to siderite. However, it may also be an artifact arising from the original derivation of 

the thermodynamic properties of goethite and siderite, which define the solubility product values 
 

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incorporated in the EQ3/6 database. Because the [Fe+2]/[H+]

2

 activity ratio is determined by the 

redox-controlling pair goethite/pyrite, the SI profile for siderite is actually defined by the 

corresponding distribution for Log P(CO2) (Fig. 3.21). Examination of the Log P(CO2) profile 

clearly indicates that this parameter has been unduly biased upscale to partial pressures that are 

unrealistically high for shallow aquifers. Thus, while correction for charge imbalance using 

HCO3

-

 is justified to account for CO2 degassing, it is inappropriate to apply such a correction in 

all cases. Unfortunately, there is no simple means by which the correction for charge imbalance 

may be applied without the inadvertent introduction of other undesired biases. 

Fig. 3.19. Histogram showing the distribution of calculated calcite saturation indices of more than 35,000 

groundwater analyses selected from the NWIS Database. Charge imbalance corrected through 

adjustment of HCO3

-

 concentration. 
 

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Fig. 3.20. Histogram showing the distribution of calculated siderite saturation indices of more than 35,000 

groundwater analyses selected from the NWIS Database. Charge imbalance corrected through 

adjustment of HCO3

-

 concentration. 

Fig. 3.21. Histogram showing the distribution of calculated Log P(CO2) of more than 35,000 groundwater 

analyses selected from the NWIS Database. Charge imbalance corrected through adjustment of HCO3

-

concentration. 
 

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The purpose of the foregoing discussion was to evaluate the significance of analytical artifacts 

primarily associated with inadvertent degassing of groundwater samples prior to alkalinity 

determinations and associated errors introduced by secondary precipitation of calcium carbonate, 

and other factors relating to the measurement of pH. By so doing, we hoped to develop a 

procedure whereby such artifacts might be removed, or at least, mitigated during the subsequent 

processing of the more than 38,000 groundwater analyses with the EQ3/6 species distribution 

code. It was not the intent of this analysis to define the parameters required for simulating CO2

intrusion in a potable water aquifer and its impact on hazardous inorganic constituent 

concentrations in the aquifer. However, the study raised a number of issues relating to the 

broader issue of geochemical model validation.

The reference to Figure 3.12 shows that if the criterion for acceptance of a groundwater chemical 

analysis were a charge imbalance of no more than 5%, then two-thirds of the 35,000 analyses 

downloaded from the NWIS database and successfully run by EQ3 would be considered 

acceptable. Table 3.11 shows that errors in the measurement of ambient pH might arise through 

inappropriate sample handling, even though the sample would pass the charge imbalance screen. 

Attempts to rectify such by restoring lost CO2 through charge balancing on HCO3

-

 were only 

partially successful. Thus, it appeared that no simple strategy could be adopted that would 

account for analytical errors without introducing unwanted biases in the evaluation. The 

following is a partial list of potential errors affecting the groundwater chemical analyses: (1) 

permitting degassing of CO2, (2) permitting precipitation of dissolved constituents, (3) failure to 

analyze all principal constituents in solution, (4) acidification of a sample containing suspended 

material, (5) permitting access of oxygen, thereby oxidizing reduced species, (6) inadvertent 

contamination both before and after sample collection, (7) inadequate calibration of pH 

electrodes, (8) contamination of sample by reference electrode electrolyte, and (9) interferences 

and biases associated with individual analytical methods for specific elements. 

It became evident that the development of a systematic strategy for correcting chemical analyses 

containing discrepant measurements would require more time and effort than could be devoted 

within this study. Furthermore, selecting only those analyses meeting certain criteria might not 
 

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necessarily improve the quality of the subsequent evaluation, because analyses might be 

eliminated that could otherwise contribute significantly to the statistics of small sample 

populations involving such hazardous constituents as antimony, which is infrequently analyzed 

in groundwaters. We decided, therefore, to include all analyses regardless of quality. Such a 

decision is likely to increase the statistical dispersion of calculated saturation indices of minerals 

controlling the solubilities of hazardous inorganic constituents, but it is unlikely to affect their 

identification, or to introduce systematic biases, as it is the mode of the distribution that is the 

critical parameter of interest. It would clearly be interesting to pursue this issue on a broader 

scale, as it has important implications regarding geochemical model validation and the degree of 

confidence attached to the results of field studies. 

3.4.4 Evaluation of Groundwater Redox State 

Estimating the redox potential of groundwaters is critical to interpretation of most hazardous 

constituents targeted in this study. As noted before, As, Sb, Se, and U can occur in differing 

oxidation states that are stable in the presence of water. Arsenic, Cd, Hg, Sb, Pb and Zn 

commonly form insoluble sulfides under reducing conditions, which become unstable under 

oxidizing conditions, where sulfur is oxidized from the -II to the 0 or the VI state. As, Sb and Se 

can also form compounds with Fe(II), which is destabilized under oxidizing conditions to the 

Fe(III) state. Furthermore, some of the hazardous constituents can form compounds with each 

other, e.g., selenides of As, Cd, Pb or Zn, or arsenates of Pb. Thus, a variety of solubilitycontrolling minerals can form, depending mainly on both the pH and redox state of the 

groundwater. 

Unfortunately, although the redox state, or Eh, of a groundwater is easily measured using a Pt 

electrode, this type of measurement is usually subject to large and unpredictable errors (Baas 

Becking et al., 1960; Lindberg and Runnels, 1984; Fish, 1993; Stefansson, 2003). Furthermore, 

not all species of an element that can occur in different oxidation states are necessarily in redox 

equilibrium, as has been observed with respect to As(III)/As(V) (Cherry et al., 1979; Johnston 

and Singer, 2007), Sb(III)/Sb(V) and Se(II)/(IV)/Se(VI) and S(II)/S(VI) (Sato, 1992). Although 
 

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the distribution-of-species code EQ3/6 can account for a lack of homogeneous equilibrium 

within the aqueous phase, and the transformation of species from one redox state to another can 

be treated kinetically in TOUGHREACT, such a refinement is beyond the scope of this study 

and not utilized in the analysis presented here. 

Instead of relying on direct Eh measurements, which, as noted above, are of questionable 

reliability, we use an indirect method to estimate redox state. Equilibration between Fe(II) and 

Fe(III) occurs relatively rapidly in the aqueous phase (Grundl and Macalady, 1989), and 

therefore advantage can be taken of measurements of these species in solution to calculate the Eh 

of solution at a given pH. While the concentration of Fe(III) in solution is usually very low and 

not normally measured, its concentration and activity can be calculated if equilibrium with a 

ferric oxide or oxyhydroxide is assumed in the aquifer. This assumption is reasonable, especially 

under oxidizing conditions. With an increasingly reduced state, more and more of the Fe(III) in 

the ferric phase would be reduced to Fe(II), which can be incorporated in the octahedral layers of 

secondary smectites or chlorites. Under even more highly reducing conditions, iron can be 

sequestered and precipitated as a sulfide, usually as pyrite. Eventually, a state would be reached 

where all Fe(III) containing minerals are dissolved, and the redox state can no longer be defined 

by the Fe(II)/Fe(III) activity ratio. 

Over 20,000 analyses in the database extracted from the NWIS contain measurable Fe analyses. 

These provide a basis for calculating the probable Eh of a significant fraction of all samples. As 

part of the NWIS database search, additional analytical data on redox pairs such as SO3

-2/HS-

, 

Mn+3/Mn+2, HCO3

-

/CH4(aq), NO3

-

/NH4

+

, etc., redox measurements, and total dissolved oxygen 

were also solicited when included in groundwater analyses containing analyses of hazardous 

inorganic constituents. Such additional information can be used to augment Eh values calculated 

using Fe analyses and to estimate the state of equilibrium or disequilibrium, but this was not 

attempted in the present study. Unlike the Fe(III)/Fe(II) couple, the other redox pairs are slow to 

achieve homogeneous equilibrium, with the possible exception of the Mn(III)/Mn(II) couple,. 

They are therefore useful in establishing whether authigenic (biogenic) processes are active in a 

potable groundwater environment, or whether the groundwater has attained a measure of 
 

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homogeneous equilibrium. Although of considerable importance (and further study would be 

merited), this subject was not explored further, owing to the relative paucity of suitable datasets 

in the groundwater quality analyses downloaded from the NWIS database. 

The following approach was taken when using Fe analyses to estimate Eh. It was initially 

assumed that goethite was present in equilibrium with the groundwater for the given chemical 

composition and pH. The total measured Fe concentration was then inserted as the Fe+2

concentration, and the Eh was computed based on the Fe(III)/Fe(II) ratio. The calculated total 

iron concentration was then compared with the measured total iron concentration according to 

the following three criteria, where Fe(o/ p) is the calculated total iron concentration and Fe(i/ p)

is the measured input concentration of total Fe: 

(a) If Fe(o / p) − Fe(i / p)

Fe(o / p) = 0 Leave unchanged, and record Eh. 

(b) If 0 05

Fe o p

Fe o p Fe i p . ( / )( / ) ( / ) ≥ − , but < 0.95 Subtract Fe(i/p) from Fe(o/p) and enter the difference as Fe+2. 

Re-run and record Eh. 

(c) If Fe(o/ p) − Fe(i/ p)

Fe(o/ p) =1 Leave unchanged, and record Eh. 

Case (a) arises when the calculated output concentration is essentially identical to the input 

concentration, i.e., when the contribution of Fe(III) to the total Fe concentration is negligible. 

Case (c) represents the situation when the measured concentration is small in relation to the 

calculated concentration of Fe(III) in equilibrium with goethite. Case (b) arises when the 

concentrations of Fe(II) and Fe(III) are approximately equivalent, and the approximations that 

total iron is predominantly Fe(II), as in Case (a), or predominantly Fe(III), as in Case (b), cannot 

be applied, and the concentration of Fe(II) must be calculated explicitly. The applicable range 

where 0.05 ≤ Fe(o / p) − Fe(i/ p)

Fe(o / p)

 < 0. 95 is established to ensure stability in the calculations, 

while also recognizing the analytical uncertainty in the measured Fe concentrations. 

An additional specification is that the measured SO4

-2 concentration is entered as the “free” 

concentration, i.e., the total concentration of sulfate species is as analyzed, and sulfide 
 

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concentrations are added in to achieve equilibrium with the sulfate at the pH and Eh conditions 

specified for each run. This adjustment reflects the fact that the analytical procedures usually 

analyze for sulfate alone rather than total sulfur. 

The calculated Eh values were then plotted against pH to produce a typical Eh–pH diagram, as 

illustrated in Figure 3.22. The plot shows that most of the groundwaters, for which measured Fe 

analyses are available, fall within a range of moderately to strongly reducing conditions. The 

upper bound is constrained by the analytical limitations for measuring Fe(total) in solution and 

falls within the goethite stability field. The lower stability limit is bounded by the decomposition 

of water and can fall within the stability field of pyrite. Quantitative estimates of Eh are therefore 

constrained by the sensitivity of analytical methods for measuring dissolved iron. Consequently, 

a bias might be introduced through this analysis, which must be taken into consideration in 

interpreting the thermodynamic controls affecting the behavior of hazardous inorganic 

constituents in groundwater (Section 3.4.5). 

 

Fig. 3.22. Calculated Eh, based on the distribution of Fe(III) and Fe(II), and the assumption that Fe(III) is in 

equilibrium with goethite, plotted against measured pH. 
 

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3.4.5 Evaluation of Solubility Controls for Hazardous Constituents 

A series of five EQ3/6 runs were made in which groundwater analyses extracted from the NWIS 

database were subject to differing initial constraints. The purpose of these runs was to determine 

whether concentrations of the selected hazardous inorganic constituents were controlled by 

certain minerals in the aquifers. A further two runs were conducted on a limited subset of waters 

containing Pb, analyzed using PLM43 and PLM48 and Cd, analyzed using PLM43 and PLM47. 

These denote analytical methods employing ICP-MS, which is the most sensitive technique 

currently available in general use and most likely to yield valid measurements of Pb and Cd 

concentrations (see Appendix A). A description of the runs is given in Table 3.12. As pointed out 

before, each run involves an automated procedure with multiple EQ3/6 simulations for a large 

number of groundwater analyses. 

Table 3.12. Input constraints for EQ3/6 species distribution runs conducted on groundwater analyses from the 

NWIS Database. 

Run Input Constraints 

0 Eh defined by goethite saturation and total dissolved iron distributed between Fe(II) and Fe(III). 

No correction for charge imbalance. 

01 Eh defined by goethite saturation and total dissolved iron distributed between Fe(II) and Fe(III). 

Charge imbalance corrected through adjustment of HCO3

-

 concentration. 

1 Eh at the measured pH buffered by the pyrite/goethite redox couple. No correction for charge 

imbalance. 

2 Eh at the measured pH buffered by the pyrite/goethite redox couple. Charge imbalance corrected 

through adjustment of HCO3

-

 concentration. 

3 Eh at the measured pH buffered by the pyrite/goethite redox couple. Charge imbalance corrected 

through adjustment of H+

 concentration. 

4 Pb analyzed using PLM43 and PLM48 and Cd analyzed using PLM43 and PLM47. Eh defined 

by goethite saturation and total dissolved iron distributed between Fe(II) and Fe(III). No 

correction for charge imbalance. 

5 Pb analyzed using PLM43 and PLM48 and Cd analyzed using PLM43 and PLM47. Eh at the 

measured pH buffered by the pyrite/goethite redox couple. No correction for charge imbalance. 
 

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The rationale for conducting these different runs is as follows. 

1. Considerable uncertainty attends the characterization of the redox state of the analyzed 

groundwaters. It is surmised that most are anoxic and have equilibrated under reducing 

conditions. However, a minor fraction of the sampled groundwaters may be oxic, 

although recognition that those waters stabilized under such conditions can be made only 

indirectly through examination of their total chemistry. 

2. Two alternative approaches are taken to better constrain the oxidation state: (a) The 

oxidation state can be determined if either the Fe(II) and Fe(III), or total Fe have been 

analyzed in solution, while it is assumed that the groundwater is saturated with respect to 

goethite. This assumption is reasonable, but goethite saturation cannot be presumed in all 

cases. Fe(II)/Fe(III) buffering might be accomplished through the presence of other ferric 

oxides or oxyhydroxides, which would modify the calculated Eh. Furthermore, acid 

stabilization of the groundwater for subsequently analyzing Fe(II) in solution must be 

made prior to exposure to air, to prevent oxidation and precipitation of Fe(III) (Singer 

and Stumm, 1968, 1970). Unfortunately, not all analyses from the NWIS database report 

either Fe(II) and Fe(III), or total Fe. Therefore, the entire dataset is not available for 

evaluation by this method. (b) The redox state is controlled by the pyrite/goethite redox 

couple in all cases. This constraint would obviously be inapplicable to oxic groundwaters. 

Furthermore, not all reducing groundwaters are necessarily buffered by the 

pyrite/goethite couple. Other couples, for example pyrite/magnetite, pyrite/hematite, 

pyrite/siderite, pyrite/pyrrhotite and pyrite/chlorite, are possible. See Section 3.4.5.1 

below. 

3. The thermodynamic analyses conducted using EQ3/6 are carried out assuming that 

homogeneous equilibrium with respect to redox conditions has been established in all 

cases. However, groundwaters of relatively young age are known not to be in 

homogeneous equilibrium, as noted in Section 3.4.3, above. As a working hypothesis, it 

is assumed that most of the groundwaters sampled are in homogeneous equilibrium. 
 

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4. The partial oxidation of sulfides can lead to a metastable equilibrium state with respect to 

native sulfur. For example, the oxidation of galena proceeds according to the following 

half-cell reaction: 

PbS = Srhombohedral + Pb+2 + 2eunder anoxic conditions where neither hydrogen peroxide (Sato, 1992) nor bacterial 

populations are available to promote the oxidation of sulfur to sulfate. We assume that 

the metastable persistence of native sulfur does not occur over the long time frames 

available for sulfide equilibration in groundwaters, because natural radioactive decay of 

ubiquitous uranium will induce a low ambient H2O2 flux to catalyze sulfur oxidation. 

5. The majority of analyses display unacceptably large charge imbalances, i.e., > ±10%. 

The probable cause is CO2 loss followed by carbonate precipitation before analysis of 

alkalinity or bicarbonate concentration, but after pH measurement. A secondary cause 

could be measurement of pH after analysis of alkalinity or bicarbonate concentration CO2

degassing. Evaluations presented in Section 3.4.4 indicate that these causes are operative. 

No simple strategy exists, however, for identifying and correcting for both causes on a 

case-by-case basis. 

6. As noted in Sections 3.3.4 and 3.3.5, the analyses of some hazardous inorganic 

constituents are suspect, because the analytical methods used are insufficiently sensitive 

to detect the low concentrations that would equilibrate with host minerals under reducing 

conditions. Because it is not easily determined whether the groundwater was exposed to 

reducing conditions, it is generally not possible to state unequivocally whether a 

particular analysis is invalid. Thus, it was decided to accept all available hazardous 

inorganic constituent analyses for preliminary evaluation. 

7. Given the uncertainties listed above, it was decided that by making a multiplicity of runs 

testing different hypothetical conditions, and examining the results, it would be possible 

to determine the most plausible thermodynamic controls affecting the concentrations of 

the selected hazardous inorganic constituents. The runs are therefore exploratory in 

nature, and aimed towards narrowing the scope of the problem. 
 

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The output histograms of the saturation indices of selected minerals from the series of runs were 

examined closely. The output of Run 0 is comparable with that of Run 01, and likewise, the 

output of Run 1 is comparable with those of Runs 2 and 3, meaning that no significant reduction 

in statistical dispersion has been obtained through charge balancing. Instead, a bias is introduced 

through either an increase in the calculated value of P(CO2) as a consequence of charge 

balancing on HCO3-, or through effects caused by an upward shift in pH through charge 

balancing on pH. Because these biases could not be justified by independent lines of inquiry and 

statistical dispersion is not improved, and also because no differences in interpretation are 

obtained, we decided to omit further discussion of runs 01, 2 and 3. The following discussion of 

results is therefore restricted to Runs 0 and 1, where charge balancing is ignored. The findings 

from Runs 4 and 5, which also omit charge balancing, are included in Section 3.4.5, below, 

where the effect of redox potential on Cd, Pb and Zn aqueous concentrations is considered. 

3.4.5.1 Discussion of Results from Run 0 

Histograms showing the calculated saturation indices for the minerals expected to control the 

selected hazardous inorganic constituents under reducing conditions (see Section 3.2), as defined 

by goethite saturation and the concentration of iron in solution, are illustrated in Figures 3.25 

through 3.33 below. However, because the redox potential is defined independently of other 

minerals assumed to contain Fe in the reduced (II) state, we start a discussion of results by 

examining how many of the sampled groundwaters might actually have been close to or at 

saturation with respect to such minerals. In Figure 3.23, we illustrate the histograms of saturation 

indices for pyrite, magnetite, siderite, and chlorite. The pyrite SI histogram attains a maximum at 

SI ~ +5, with a broad plateau extending to SI ~ -20. This suggests that some groundwaters are 

near or at saturation with respect to this mineral. Both the spread in values and the lengthy tail 

extending to SI ~ -70 are open to several interpretations. The pronounced skew towards negative 

values suggests a range of oxidation states in the subset of samples for which Fe analyses exist. 

The sharp cutoff at SI ~ +10 reflects a natural limitation in realistic aqueous concentrations of 

Fe, but also implies a considerable analytical uncertainty associated with quantitative 

measurements for SI(pyrite). Another explanation is that the assumed homogeneous equilibrium 
 

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with respect to the SO4

-2/HS-

 couple is not applicable to many of the groundwater samples, or 

that some or all of the aqueous Fe(II) oxidized and precipitated prior to analysis. Such questions 

should be investigated further. Despite the substantial uncertainties associated with this analysis, 

it is nevertheless plausible to assume that many of the samples had equilibrated with pyrite, the 

uncertainties all suggesting a higher proportion than is indicated in Figure 3.23a. 

The spread in SI values for magnetite (Figure 3.23b) is much more restricted than for pyrite, 

although a negative skew is again observed. The frequency of SI values peaks at about zero, 

which is strongly suggestive that a magnetite/goethite or magnetite/hematite buffer might also 

participate in controlling the oxidation state of many of the groundwaters. In contrast, siderite SI 

values in Figure 3.23c peak at about -2, indicating that the presence of this mineral is less 

common, and that the likelihood that it could buffer the oxidation state with either goethite or 

hematite is small. Note that this figure differs from that of Figure 3.16, which also displays a SI 

frequency histogram for siderite, because the latter was determined assuming the coexistence of 

pyrite and goethite and a calculated activity of Fe+2. 

Another possible redox buffer involves the coexistence of chlorite with goethite, hematite, 

magnetite, or possibly pyrite. The stability relations between chlorite and the cited iron-bearing 

minerals are complicated, because this mineral is a solid solution where Mg+2 substitutes for Fe+2

in the structure, whose end members are respectively identified by the minerals clinochlore and 

daphnite. The calculated SI for chlorite, where Mg+2/(Mg+2 + Fe+2) = 0.5, indicates significant 

supersaturation with SI ~ 5, as shown in Figure 3.23d. However, the calculations assume 

saturation with respect to opal-CT, which would supersaturate the groundwater with respect to 

quartz. Correction for silica saturation to quartz and normalization of the SI to unit Fe 

stoichiometry results in SI(chlorite) ~ +1, which is reasonably close to saturation, given the 

potential uncertainties associated with the derivation of the thermodynamic properties for this 

mineral. It should be noted that the precipitation of Fe in pyrite usually results in depletion of Fe 

in the chlorite, and shifting of the chlorite composition towards end-member clinochlore. 
 

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(a) (b) 

(c) (d) 

Fig. 3.23. Frequency Distribution of Saturation Indices of (a) Pyrite, (b) Magnetite, (c) Siderite and (d) Chlorite. 

Saturation Indices are calculated assuming Eh to be defined by Fe(III)/Fe(II) in solution in the 

presence of goethite, using a selected set of groundwaters from the NWIS. 

The calculated SI histogram for clinochlore, Figure 3.24, shows a somewhat similar profile to 

chlorite, but is shifted to a somewhat lower degree of supersaturation (≈ 3). Allowing for 

corrections for SIO2(aq) and possible sympathetic errors in the thermodynamic properties of both 

clinochlore and chlorite (Appendix C), it is reasonable to suggest that clinochlore might 
 

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preferentially coexist with pyrite when the latter occurs in the absence of ferric minerals in 

potable water aquifers. Further investigation of this association would be warranted. 

Fig. 3.24. Frequency Distribution of Saturation Indices of Clinochlore. Clinochlore saturation is not significantly 

affected by variation in Eh. 

We now follow with a discussion of the calculated SI frequency histograms of minerals that 

might control the activities of the hazardous inorganic constituents selected for evaluation. They 

are given in the alphabetical order of their chemical symbols, i.e., As, Ba, Cd, Hg, Pb, Sb, Se, U 

and Zn. Candidate minerals have been previously identified in Section 3.2. 

Arsenic. Figures 3.25a, b, and c show the histograms of saturation indices for arsenopyrite 

(FeAsS), scorodite (FeASO4.2H2O), and symplesite (Fe(II)3(AsO4)2·8H2O), respectively. The 

histogram for arsenopyrite Figure 3.25a displays a bimodal distribution. The smaller peak on the 

right hand side has a broad maximum at SI ~ 0, suggesting arsenopyrite solid solution in pyrite, 

as is often observed in the field. The broad distribution of values about SI(max) ~ -50 relates to 

conditions where As(V) species predominate under oxidation states too high to support the 

presence of pyrite. The scorodite and symplesite histograms, in contrast, show no evidence that 
 

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any groundwaters attain saturation with respect to either mineral. This finding is in conflict with 

the postulated existence of a scorodite-like phase in groundwaters subject to oxidation, as 

suggested by Thornberg and Sahai (2004). Qa

(a) (b) 

 (c) 

Fig. 3.25. Frequency Distribution of Saturation Indices of (a) Arsenopyrite, (b) Scorodite, and (c) Symplesite. 

The Saturation Indices are calculated using a selected set of groundwaters from the NWIS and 

assuming Eh to be defined by Fe(III)/Fe(II) in solution. 
 

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Barium. Figures 3.26a and b display SI histograms for barite (BaSO4) and witherite (BaCO3). 

These two minerals are isostructural with the analogous Pb minerals, anglesite (PbSO4) and 

cerussite (PbCO3), which are compared separately, below. Because barium is present only in one 

oxidation state in natural groundwaters, solubility control is essentially independent of Eh, unless 

all sulfate in the system is reduced to sulfide. Such conditions can arise in coal beds where Ba is 

observed associated with silicates or phosphates, e.g., crandallite ((Ca, Ba)Al3(PO4)2(OH)5·H2O) 

(Finkelman, 1981; Brownfield et al., 2005), or during hydrolysis by diabase, when the presence 

of witherite is favored. From Figure 3.26a, it is evident that barium solubility is controlled by the 

presence of barite where SI(max) ~ 0, rather than by witherite, which is undersaturated (SI(max) 

≈ -3). 

(a) (b) 

Fig. 3.26. Frequency Distribution of Saturation Indices of (a) Barite and (b) Witherite. The Saturation Indices are 

calculated for a selected set of groundwaters from the NWIS. Eh is defined by Fe(III)/Fe(II) in 

solution, which generally does not significantly affect either barite or witherite saturation. 
 

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Cadmium. A broad distribution of SI’s for CdS (cadmium sulfide, or greenockite) is illustrated 

in Figure 3.27a. The peak reflects (a) the dispersion of analytical values in the calculation of 

SI(CdS), (b) false positive readings due to the application of insufficiently sensitive methods, (c) 

uncertainty in the calculated solubility product of CdS, and finally (d) uncertainties associated 

with the identities and thermodynamic properties of Cd sulfide and selenide complexes in 

solution in the EQ3/6 distribution. The sharp peak distribution at SI ~ 0 strongly suggests that at 

least some of the samples are saturated with respect to greenockite. However, as noted in Section 

3.4.5.2 below, this apparent saturation can be an artifact resulting from the omission of selenide 

complexing due to the absence of Se analyses in some of the groundwaters for which Cd 

analyses have been made. Therefore, the possibility that CdS exists only in solid solution with 

sphalerite cannot be excluded on the basis of the present analysis. 

Figure 3.27b illustrates the calculated SI distribution for cadmoselite (CdSe), an example where 

two separate trace hazardous constituents are essential components of the same mineral. The 

distribution is much narrower for the selenide than for the corresponding sulfide, because the 

Se(II) state is stable over a wider range of oxidation potentials defined in this case by 

Fe(III)/Fe(II). However, it is interesting to note that SI(max) ~ 0, suggesting that this mineral 

could actually control Cd(II) activity in the majority of potable groundwaters. 

The SI histogram for otavite (CdCO3) is given in Figure 3.27c. Otavite is generally believed to 

be the most stable phase containing Cd as an essential component under oxidizing conditions 

(Carroll et al., 1998; McGrath et al., 2007). The histogram indicates that almost all groundwaters 

analyzed for Cd, and passing the screen for Run 0, are undersaturated with respect to this 

mineral. 
 

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(a) (b) 

 (c) 

Fig. 3.27. Frequency Distribution of Saturation Indices of (a) CdS (Greenockite), (b) Cadmoselite and (c) 

Otavite. The Saturation Indices are calculated using a selected set of groundwaters from the NWIS. Eh 

is defined by Fe(III)/Fe(II) in solution, which generally does not significantly affect otavite saturation. 
 

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Mercury. The distribution of SI’s for cinnabar, tiemannite (HgSe), and quicksilver are illustrated 

in Figure 3.28a–c. The distribution for cinnabar is similar to that of CdS, but a significant 

fraction is supersaturated in those groundwater samples that were analyzed for Hg, with SI(max) 

being achieved at SI ~ +1. The SI distribution for tiemannite is ill defined, because of the limited 

number of analyses conducted for both Hg and Se. However, most of the available 

determinations fall between SI = 0 and –1, suggesting that this phase might form, especially in 

Se rich environments. 

In conformity with observations for CdS regarding interpretations concerning the saturation of 

this phase in groundwaters, similar arguments can be presented for caution in accepting the 

corresponding findings for cinnabar and tiemannite. The peaks reflect (a) the dispersion of 

analytical values in the calculation of SI for cinnabar and tiemannite, (b) false positive readings 

caused by the application of insufficiently sensitive methods, (c) uncertainty in the calculated 

solubility products of cinnabar and tiemannite, and finally (d) uncertainty regarding the identities 

and thermodynamic properties of Hg sulfide and selenide complexes in solution in the EQ3/6 

distribution (see Appendices D and E). 

The findings presented here are inconsistent with field observations by Benoit et al (2003) on 

shallow organic-rich sediments, and by Huerta-Diaz and Morse (1992) on shallow marine 

sediments, who show, respectively, that Hg activity is unsaturated with respect to cinnabar, and 

that Hg is preferentially incorporated in precipitating pyrite, rather than forming a discrete solid 

phase where Hg is an essential component. However, both field environments reflect transient 

authigenic conditions where metastable phases are likely controlling the aqueous solubility of 

Hg, and where long-term evolution of the system, as postulated by the Ostwald Rule of stages, 

will lead to recrystallization of pyrite and expulsion of Hg from the lattice involving the 

formation of metastable phases. Both cinnabar and tiemannite have been observed in coal 

(Tewalt et al., 2001; Brownfield et al., 2005; Kolker et al., 2006), and it is therefore probable 

that, given sufficient time, the natural environment evolves and saturation is achieved with 

respect to either or both minerals in shallow reducing groundwaters. 
 

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Also included in Figure 3.28 is the SI histogram for quicksilver, which shows a bimodal 

distribution. The secondary peak at SI ~ -1 probably reflects the true saturation state of 

quicksilver under reducing conditions. The supersaturation where SI(max) ~ +9 is indicative of 

artifacts caused by an erroneously elevated oxidation state calculated from the Fe(III)/Fe(II) 

ratio. Comparison should be made with the data provided by Run 1, in Section 3.4.5.2 below. 

(a) (b) 

(c) 

Fig. 3.28. Frequency Distribution of the Saturation Index of (a) Cinnabar, (b) Tiemannite, and (c) Quicksilver. 

The Saturation Indices are calculated using a selected set of groundwaters from the NWIS and 

assuming Eh to be defined by Fe(III)/Fe(II) in solution. 
 

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Lead. The thermodynamic control of Pb by galena (PbS) was assumed in a recent study to 

evaluate the impact of elevated P(CO2) on hazardous inorganic constituent concentrations in 

groundwaters (Wang and Jaffe, 2005), and provided a motivation for the current project. 

Therefore, the general behavior of Pb in groundwater, and of galena in particular, deserves 

special attention. Figures 3.29a through 3.29f illustrate the SI distributions for galena and 

clausthalite (PbSe), either of which might control the Pb activity in reducing groundwaters, and 

for five other minerals that might potentially control the Pb activity in oxic aquifers: cerussite 

(PbCO3), hydrocerussite (Pb3(CO3)2(OH)2), plumbonacrite (Pb10(CO3)6O(OH)6), anglesite 

(PbSO4) and mimetite (Pb5(AsO4)3Cl). Another relatively insoluble Pb mineral, pyromorphite 

(Pb5(PO4)3Cl), the phosphate analogue of mimetite, was not examined, because no special effort 

was made to acquire and evaluate phosphate analyses in the NWIS groundwater quality data. 

The distribution of galena SI’s suggests that a significant fraction of the groundwaters is 

saturated with respect to this mineral, as a clearly defined maximum frequency in SI is observed 

at SI ~ 0 in Figure 3.29a. In common with cadmoselite, clausthalite displays a tight dispersion in 

SI with SI(max) ~ 0. Also, as noted above, the stability field for Se(II) extends to higher 

oxidation states at a given pH than does S(II), thereby restricting the broader distribution shown 

by galena. The SI distribution profile for clausthalite strongly suggests that this phase may 

coexist with or supplant galena in potable water aquifers. The dominance of clausthalite over 

galena in Appalachian coal deposits is suggestive that this might be the case. 

The findings regarding Pb carbonate minerals, and anglesite and mimetite, indicate that, with the 

exception of a very small fraction of groundwaters saturated with respect to cerussite, these 

minerals all show moderate to substantial degrees of undersaturation, and appear to be 

implausible candidates for control of Pb activity in all groundwaters studied. However, either 

cerussite or hydrocerussite saturation would be expected, depending on pH, if galena in an 

aquifer were to be oxidized. Taken together, these observations suggest that most groundwaters 

are sufficiently reducing to stabilize galena. 
 

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(a) (b) 

(c) (d) 

 Fig. 3.29. (Part 1) 
 

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(e) (f) 

 

 (g) 

 

Fig. 3.29. Frequency Distribution of Saturation Indices of (a) Galena, (b) Clausthalite, (c) Cerussite, (d) 

Hydrocerussite (e) Plumbonacrite (f) Anglesite and (g) Mimetite in a selected set of groundwaters from 

the NWIS. The Saturation Indices are calculated assuming Eh to be defined by Fe(III)/Fe(II) in 

solution. Cerussite and anglesite saturations are not significantly affected by variations in Eh. 

Finally, it should be mentioned that a significant array of naturally occurring, but obscure Pb 

silicates formed mainly under epithermal conditions have been described in the literature. 

Nothing is known of their solubilities or other thermodynamic properties, and thus it is 
 

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impossible to state that any one of these minerals might also control Pb activity in groundwaters, 

where oxidation has destabilized either galena or clausthalite. Although remote, the possibility 

exists that one of these minerals, as with hemimorphite controlling Zn, might likewise control 

Pb. 

Antimony. Histograms of the saturation indices of selected antimony minerals, which might 

control the activity of Sb in potable groundwaters, are presented in Figure 3.30a-e. Gudmundite 

(FeSbS) is the antimony analogue of arsenopyrite and, like its As counterpart, is believed to enter 

in solid solution in pyrite. Gudmundite, berthierite (FeSb2S4), and stibnite (Sb2S3) are antimony 

minerals found in epithermal ore deposits of antimony, the latter being the primary and most 

abundant antimony ore mineral. Antimonselite (Sb2Se3), the selenium analogue of stibnite, is 

rarely observed in nature, but may control Sb activity in potable water aquifers. Kermesite 

(Sb2S2O), or red antimony, is commonly found as a supergene alteration product in antimony 

ores, but occasionally occurs as a primary mineral (Williams-Jones and Normand, 1997). 

Interpretation of Sb activity control by candidate minerals is complicated by uncertainties 

regarding the validity of some of the Sb analyses in groundwaters, by the sparsity of data, and by 

uncertainties regarding the validity of the oxidation state defined by Fe(III)/Fe(II) where Fe 

analyses are of uncertain quality. From the distribution of measured concentrations illustrated by 

Figure 3.6f, we might infer that meaningful concentrations of Sb in NWIS groundwaters fall 

between –5 and -3 (in Log mg/L), and that concentrations above this range are artifacts resulting 

from the application of insufficiently sensitive analytical methods. However, as noted elsewhere, 

carte blanche rejection of questionable analyses cannot be justified through speculation alone, 

and therefore all available Sb measurements are used in the present analysis. In the following 

discussion, we recognize the limitations imposed by both the analytical methods and the sparsity 

of measurements. 
 

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(a) (b) 

(c) (d) 

 Fig. 3.30. (Part 1) 
 

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(e) 

Fig. 3.30. Frequency Distribution of Saturation Indices of (a) Gudmundite, (b) Berthierite, (c) Stibnite, (d) 

Antimonselite and (e) Kermesite. The Saturation Indices are calculated using a selected set of 

groundwaters from the NWIS, and assuming Eh to be defined by Fe(III)/Fe(II) in solution. 

Figures 3.30a, b, c, and d, showing the SI histograms for gudmundite, berthierite, stibnite, and 

antimonselite respectively, all suggest that Sb activity in groundwaters is unlikely to be 

controlled by their presence as discrete phases, since the majority of SI’s suggest moderate to 

extreme undersaturation. However, for all four minerals, local maxima are observed at SI ~ 0. 

Under rare situations where strongly reducing conditions prevail, one of these minerals could be 

present, coexisting with pyrite or another iron sulfide phase at a given pH (Williams-Jones and 

Normand, 1997). Further analysis to substantiate this conjecture is presently beyond the scope of 

this study. In contrast to the antimony minerals referred to above, a significant population of SI’s 

for kermesite is in positive territory. It is therefore probable that this mineral controls Sb activity 

in potable groundwaters. 
 

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Selenium. Selenium ranks as particularly important, not only as a hazardous constituent in its 

own right, but because of its potential as Se(-II) to complex strongly with other hazardous 

constituents in solution and to form extremely insoluble selenides. In Figure 3.31, we assemble 

SI histograms of all potential activity controlling selenides under consideration, i.e., ferroselite 

(FeSe2), cadmoselite, tiemannite, clausthalite, and antimonselite. 

The SI histogram for ferroselite displays a bimodal distribution with SI(max) at SI ~ -17 and –3. 

A similar distribution is also displayed in Run 1 (Figure 3.40), where SI(max) ~ -16 and –7.5. 

The less negative peak probably represents Se substitution for S in pyrite and indicates that 

insufficient Se is present in most potable water aquifer host rocks for saturation with respect to 

ferroselite. Spectroscopic studies have revealed the presence of dzharkenite, a ferroselite 

polymorph coexisting with selenian pyrite in shales associated with Western phosphate deposits 

(Ryser et al., 2005). However the average concentration of Se in these shales approaches 300 

mg/kg, which is over three orders of magnitude greater than is believed to occur in most 

domestic potable water aquifer host rocks (see Section 3.6). An explanation for the peak centered 

about SI ~ -17 is not presently forthcoming. 

Cadmoselite, tiemannite and clausthalite all appear to be at or close to saturation, suggesting that 

these selenides could co-exist with or supplant the corresponding sulfide, provided that the Gibbs 

Phase Rule is obeyed at equilibrium. The very limited SI data available for antimonselite 

suggests that this mineral, like its sulfide analogue, stibnite, is unlikely to be present and control 

Sb activity in NWIS potable groundwaters. However, it is interesting to speculate whether Se 

analogues of kermesite with formulas such as Sb2SSeO or Sb2Se2O, although never observed or 

described, might be present in small quantities. Further study to define their stability fields as 

functions of Sb and Se abundance and pH and Eh is merited. 
 

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(a) (b) 

(c) (d) 

 Fig. 3.31. (Part 1) 
 

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(e) 

Fig. 3.31. Frequency Distribution of Saturation Indices of (a) Ferroselite, (b) Cadmoselite, (c) Tiemannite, (d) 

Clausthalite, and (e) Antimonselite. The Saturation Indices are calculated using a selected set of 

groundwaters from the NWIS, and assuming Eh to be defined by Fe(III)/Fe(II) in solution. 

Uranium. Figure 3.32 gives SI histograms for uraninite (UO2), coffinite (USiO4) and a typical 

and fairly common oxidation product of uraninite, uranophane (Ca(UO2)2SiO3(OH)2.5H2O). A 

relatively sharp peak in the frequency histogram for uraninite (UO2) is observed at SI ~ +1. A 

similar result is obtained for coffinite at SI ~ +1.5. Bearing in mind that SiO2(aq) had been set to 

be in equilibrium with respect to opal C-T, and that the SiO2(aq) activity for this silica 

polymorph is about 5 times higher than for the most stable polymorph, quartz, these results 

suggest that U activity in solution is probably controlled by either uraninite, coffinite, or both. 

The reason for the observed modest degree of supersaturation is not known, and further review to 

investigate the cause is warranted. This finding is consistent with field observations of sediment 

hosted uranium deposits, where the co-existence of the two minerals is not uncommon (AbdelGaward and Kerr 1961; Drew Ilger et al., 1987). Furthermore, the finding is strongly indicative 

that most groundwaters are sufficiently reducing to stabilize one or both minerals. The 

histograms of uraninite and coffinite can be compared with that for uranophane, where U is 

present in the (VI) instead of the (IV) state. It is obvious that all groundwaters examined are 
 

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unsaturated with respect to this mineral, further substantiating the conclusion that the 

groundwaters are sufficiently reducing to stabilize U+4 instead of UO2

+2. 

(a) (b) 

(c) 

Fig. 3.32. Frequency Distribution of the Saturation Index of (a) Uraninite, (b) Coffinite and (c) Uranophane. The 

Saturation Indices are calculated using a selected set of groundwaters from the NWIS, and assuming 

Eh to be defined by Fe(III)/Fe(II) in solution. 
 

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Zinc. Figures 3.33a, b, c, and d provide the SI histograms for sphalerite (ZnS), smithsonite 

(ZnCO3), hydrozincite (Zn5(CO3)2(OH)6) and hemimorphite (Zn4Si2O7(OH)2.H2O). 

(a) (b) 

(c) (d) 

Fig. 3.33. Frequency Distribution of the Saturation Indices of (a) Sphalerite, (b) Smithsonite, (c) Hydrozincite 

and (d) Hemimorphite. The Saturation Indices are calculated , using a selected set of groundwaters 

from the NWIS, and assuming Eh to be defined by Fe(III)/Fe(II) in solution. 
 

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The histogram for sphalerite shows a maximum at SI ~ +2. This observation suggests that the Zn 

concentration in reducing groundwaters could be controlled by sphalerite. The reason for the 

positive SI deviation from zero is not clear. Interpretation is further complicated by the 

observation that the SI frequency for hemimorphite, SI ~ +2. The SiO2(aq) activity in the 

analysis is defined by opal-CT, which is approximately 0.7 log units higher than that for quartz 

saturation. For groundwaters saturated with respect to quartz, the saturation index would be 

approximately zero. This suggests that control of Zn activity in groundwaters may be determined 

by either sphalerite and/or hemimorphite. Control of Zn activity by smithsonite or hydrozincite 

appears to be unlikely in all but significantly alkaline groundwaters. 

3.4.5.2 Discussion of Results from Run 1 

A spread of Eh values is observed in Run 0 above, depending on pH and the availability of 

Fe(II), Fe(II) plus Fe(III), or Fe(total) analyses, together with the assumption of goethite 

saturation in all analyses. Run 1, in contrast, defines the Eh at a given pH through the 

coexistence of goethite and pyrite, which constitutes an Eh buffer. It is thus assumed that all 

groundwaters are reducing, even though it is probable that a minor fraction might actually be 

oxidizing. This concession to expediency in the absence of feasible methods for characterizing 

the oxidation state of groundwaters other than through the method adopted for Run 0 should be 

borne in mind when considering the following analysis of thermodynamic controls. 

Arsenic. SI histograms for arsenopyrite, scorodite, and symplesite are given in Figures 3.34a, b 

and c. They differ substantially from those generated under Run 0, because Eh is now tightly 

constrained, and the variability induced by transitions in oxidation state from As(III) to As(V) is 

no longer a factor. The SI profile for arsenopyrite peaks at SI ~ -3.5, suggesting that As activity 

is controlled primarily by arsenopyrite in solid solution in pyrite (arsenian pyrite). Neither 

scorodite nor symplesite could be present in the aquifers from which the sampled population of 

potable groundwaters are taken, since both are strongly undersaturated. 
 

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(a) (b) 

(c) 

Fig. 3.34. Frequency Distribution of Saturation Indices of (a) Arsenopyrite, (b) Scorodite, and (c) Symplesite. 

The Saturation Indices are calculated using a selected set of groundwaters from the NWIS, and 

assuming Eh to be defined by the coexistence of pyrite and goethite. 

Barium. The histogram for barite, Figure 3.35, is similar to that of Figure 3.26a. The profile 

clearly shows a negative skew rather than being symmetrical. The latter would be expected if all 

groundwaters were saturated with respect to barite, thus suggesting a bias towards 

undersaturation in the population of sampled groundwaters. Presentation of the corresponding 
 

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histogram for witherite is omitted, since it would be essentially identical to that displayed under 

barium in Run 0. 

Fig. 3.35. Frequency Distribution of the Saturation Index of Barite. The Saturation Indices are calculated for a 

selected set of groundwaters from the NWIS. Eh is defined by the coexistence of pyrite and goethite, 

but does not significantly affect barite saturation. 

Cadmium. The CdS SI histogram, Figure 3.36a, displays a trimodal distribution with SI maxima 

at ~ -3, -0.5, and +1. This complex distribution is probably in part an artifact caused by the bimodal distribution of analyzed Cd concentrations (Figure 3.6d). The analysis conducted in 

Appendix B2 suggests that SI(max) ~ -0.5 is more credible, and is supported by further selection 

and evaluation of only those groundwater analyses conducted using the most sensitive analytical 

methods under Runs 4 and 5 (see Section 3.4.6, below). The cause of the peak where SI(max) ~ 

-3 might be explained by the presence of competitive selenide complexing depressing the 

activity of Cd+2 in those groundwater samples also reporting Se. If this supposition is true, then 

saturation with respect to CdS is not achieved, and the Cd+2 activity reflects a solid solution in 

sphalerite and/or galena. However, the possibility remains that greenockite could be saturated at 

potable water aquifer temperatures, because solid solution substitution in sphalerite and/or galena 

would decrease with falling temperature. Further examination of the water quality analyses, and 

evaluation of the temperature-dependent behavior of Cd substitution in sphalerite and/or galena, 
 

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are needed to resolve this matter. The SI histogram for cadmoselite in Figure 3.36b is relatively 

well defined, with SI(max) at ~ 0, although a small scattering of SI values occur between SI = -4 

and –1. Saturation suggests that Cd+2 activity may actually be controlled by the presence of 

cadmoselite. The findings are consistent with those of Run 0 in Figure 3.27b. 

(a) (b) 

Fig. 3.36. Frequency Distribution of Saturation Indices of (a) CdS (Greenockite) and (b) Cadmoselite. The 

Saturation Indices are calculated assuming Eh to be defined by the coexistence of pyrite and goethite, 

using a selected set of groundwaters from the NWIS. 

Mercury. Figures 3.37a-c show the SI histograms for cinnabar, tiemannite and quicksilver. Peak 

frequencies in SI for all three minerals are generally consistent with the findings of Run 0 

(Figure 3.28). The supersaturation revealed in the histogram for cinnabar, where SI ~ 1.0, 

indicates its likely presence in aquifers, whereas tiemannite could also be present occasionally, 

as SI(max) ~ -0.5, and therefore close to saturation. The observed saturation with respect to 

quicksilver (SI(max) ~ 0) is inconsistent with the reducing conditions imposed by the 

goethite/pyrite buffer. Taken together, the supersaturation with respect to cinnabar and saturation 

with respect to quicksilver suggest an error in calculated Hg+2 activity by approximately one log 

unit. Several alternative explanations are possible, and further refinement of the analysis in this 

report is needed. However, uncertainties associated with the thermodynamic parameters used in 

the analysis (see discussion under mercury in the preceding Section 3.4.5.1) preclude drawing a 
 

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definitive conclusion at this time. If a systematic error in Hg+2 activity were confirmed, then SI 

(tiemannite) would fall to ~ -1.5, and would be unlikely to be present in other than Se-rich 

environments. 

(a) (b) 

(c) 

Fig. 3.37. Frequency Distribution of the Saturation Index of (a) Cinnabar, (b) Tiemannite and (c) Quicksilver. 

The Saturation Indices are calculated assuming Eh to be defined by the coexistence of pyrite and 

goethite, using a selected set of groundwaters from the NWIS. 
 

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Lead. SI histograms for galena, clausthalite and cerussite are presented in Figures 3.38a, b, and 

c. The histogram for galena is represented by a ragged single peak with a maximum at about SI ~ 

+1.0. In contrast to the corresponding histogram for Run 0 (Figure 3.29a), the dispersion is far 

smaller owing to the uniform treatment whereby all analyses are saturated with respect to 

goethite and pyrite. The maximum frequency for clausthalite is at SI ~ -0.5, which compares well 

with Figure 3.29b from Run 0. Both analyses support the belief that either galena, clausthalite, or 

both could control the Pb+2 activity in potable groundwaters. 

(a) (b) 

(c) 

Fig. 3.38. Frequency Distribution of Saturation Indices of (a) Galena, (b) Clausthalite and (c) Cerussite. Eh is 

defined by the coexistence of pyrite and goethite, but does not significantly affect cerussite saturation. 
 

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The SI histogram for cerussite indicates that this mineral does not control Pb activity in most 

groundwaters under reducing conditions. However, the small secondary peak at SI ~ 0 suggests 

that a small percentage, ~ 5%, of potable groundwaters could be saturated with respect to this 

mineral. It should be noted that, were this to be the case, the groundwater would be oxidizing, 

and the assumption of goethite/pyrite equilibrium would no longer be applicable. 

Antimony. SI histograms for gudmundite, stibnite, antimonselite and kermesite are presented in 

Figures 3.39a, b, c, and d. The very sparse dataset represented in the corresponding Figures 

3.30a, b, c, and e is augmented in Run 1, because a significant number of Sb analyses were 

eliminated in Run 0, due to the absence of analyses for Fe in the same groundwater sample. 

Nevertheless, the results are largely comparable and the conclusions reached under the 

discussion for antimony in Section 3.4.5.1 are generally applicable. The bimodal distributions, 

especially evident with respect to stibnite and kermesite, result from the bimodal distribution in 

Sb concentration observable in Figure 3.6f. As discussed elsewhere in this report (Section 3.3.5, 

Appendix B2), the more positive peaks in SI are artifacts caused by the application of 

insufficiently sensitive analytical methods, and therefore reference should be made to the more 

negative peak. The histogram for gudmundite, Figure 3.39a, is more clearly defined than its 

counterpart, Figure 3.30a, due to the constraints imposed on Eh by the pyrite/goethite couple. All 

SI values are negative and peak at SI(max) ~ -5. The result is consistent with the likely solid 

solution of the gudmundite component in pyrite, in analogy with the corresponding arsenopyrite 

solid solution. The stibnite and antimonselite histogram shows SI(max) ~ -13 and -20, 

respectively, indicative that neither mineral is likely to be present in potable water aquifers. The 

peak value in SI frequency for kermesite occurs at SI ~ -0.5, which again suggests its potential 

occurrence as a mineral controlling the activity of Sb(III) in solution, especially in aquifers 

deficient in pyrite. 
 

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(a) (b) 

(c) (d) 

Fig. 3.39. Frequency Distribution of Saturation Indices of (a) Gudmundite, (b) Stibnite, (c) Antimonselite and (d) 

Kermesite. The Saturation Indices are calculated assuming Eh to be defined by the coexistence of 

pyrite and goethite, using a selected set of groundwaters from the NWIS. 

Selenium. SI histograms for all selenium minerals are again assembled in Figures 3.40. The 

ferroselite SI histogram gives a bimodal distribution, potentially attributed to the omission of 

analyses of species in solution that complex selenium, i.e., Cd, Hg, Pb, Zn, thus giving rise to 
 

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elevated HSe- activities in the groundwaters. However, this artifact requires further confirmation. 

The profile at SI(max) ~ -15 probably represents the true state of saturation with respect to 

ferroselite in the evaluated set of NWIS groundwaters. With the exception of antimonselite, the 

remaining minerals all appear to be close to or at saturation, and therefore could be effective in 

controlling SE(II) activity in potable groundwaters. 

(a) (b) 

(c) (d) 

Fig. 3.40 Part 1 
 

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(e) 

Fig. 3.40. Frequency Distribution of Saturation Indices of (a) Ferroselite, (b) Cadmoselite, (c) Tiemannite, (d) 

Clausthalite and (e) Antimonselite. The Saturation Indices are calculated assuming Eh to be defined by 

the coexistence of pyrite and goethite, using a selected set of groundwaters from the NWIS. 

Uranium. Results for uranium in Figure 3.41 are similar to those of Run 0. Uraninite shows SI ~ 

+1, whereas coffinite shows SI ~ +1.5. In correcting for SiO2(aq) from saturation with respect to 

opal-CT and quartz, coffinite SI would be approximately +0.7, which suggests that the 

occurrence frequency of coffinite would be comparable to that of uraninite, consistent with field 

observations of sediment-hosted uranium deposits (Abdel-Gaward and Kerr, 1961; Drew Ilger et 

al., 1987). 

Zinc. The single peak in SI in the histogram for sphalerite, Figure 3.42, is consistent with the 

conservative assumptions of Run 1, leading to SI ~ +1, which is comparable with that given by 

Run 0. The negative skew suggests that not all groundwaters are saturated with respect to 

sphalerite. The histogram for hemimorphite is also given, even though this mineral is more likely 

to be present under more oxidizing conditions where pyrite is no longer stable. The peak SI 

frequency occurs at SI ~ -2, suggesting that hemimorphite is less likely to be found coexisting 

with sphalerite and pyrite, even though the relative stabilities for all three minerals are 

comparable. 
 

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(a) (b) 

Fig. 3.41. Frequency Distribution of the Saturation Index of Uraninite. The Saturation Indices are calculated 

assuming Eh to be defined by the coexistence of pyrite and goethite, using a selected set of 

groundwaters from the NWIS. 

(a) (b) 

Fig. 3.42. Frequency Distribution of the Saturation Index of (a) Sphalerite, and (b) Hemimorphite. The 

Saturation Indices are calculated assuming Eh to be defined by the coexistence of pyrite and goethite, 

using a selected set of groundwaters from the NWIS. 
 

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3.4.6 Variation of Lead, Cadmium, and Zinc Concentration with Redox 

Potential 

A wealth of information has been presented in the previous ections (3.4.5.1 and 3.4.5.2), and 

apparent conflicts between the alternative distributions of SI’s for the same mineral have been 

observed, as different specified equilibrium conditions are compared, especially in relation to 

defining the redox potential, or Eh, of the system. As is noted at the outset of this section, the 

purpose of conducting alternative runs is to gain insight into the chemical controls affecting the 

collective set of all groundwaters selected for geochemical analysis in the face of uncertainties in 

the quality of the analyses, uncertainties in the identities of aqueous species and minerals that 

may control the activities of hazardous inorganic constituents in solution, uncertainties in the 

quality of thermodynamic data used to calculate the mineral solubility products and aqueous 

species dissociation constants in the EQ3/6 Data0.dat database, and uncertainties defining the 

redox potential of the groundwaters when in situ. Despite the uncertainties, the results generally 

show a consistent pattern, allowing the investigator to use expert judgment in reaching a number 

of reasonable conclusions. 

The principal finding drawn from a comparison of the results obtained in Runs 0 and 1 is that the 

majority of groundwaters are reducing, and that the assumption that they are buffered by the 

pyrite/goethite couple is probably a good approximation, even though more refined evaluations 

considering other natural redox buffers are justified in future studies. The large variations in 

redox state generated through the assumption of goethite equilibrium with respect to total Fe in 

solution causes substantial and unrealistic scatter in data relating to certain sulfide minerals, 

especially pyrite, galena, kermesite, and CdS. Furthermore, a comparison between cinnabar and 

quicksilver saturation profiles suggests that the unrealistically high levels of quicksilver 

saturation are the result of treating a solution in equilibrium with cinnabar, and containing 

abundant Hg sulfide complexes in solution, that are not taken into account because an 

erroneously elevated redox potential has been specified. Likewise, the extreme degrees of 

undersaturation of arsenates, as well as the low concentrations of Cd, Pb, and U, and their 

undersaturation with respect to alteration products under mildly reducing conditions—all suggest 
 

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that the groundwaters were reducing and that the Fe analyses in these groundwaters were 

erroneously low. This is hardly surprising, given that neutral reduced-state groundwaters, if 

exposed to air, will immediately lead to oxidation and precipitation of any Fe(II) present. Thus, 

we believe that the results presented for Run 1 are more credible and can be relied upon with 

greater confidence than those of Run 0. However, this assertion is made with the caveat that a 

small proportion of the groundwaters with an imposed pyrite/goethite redox buffer may in fact 

have been drawn from an aquifer where oxidizing conditions actually prevailed. 

Some of the issues involved in variations between reducing and oxidizing conditions are 

discussed with respect to Pb, cadmium, and zinc in the following paragraphs. 

Lead. Figure 3.43 illustrates some of the controls that determine Pb concentration in 

groundwaters. The solubilities of galena and cerussite in a representative groundwater with a pH 

= 7.6 (a pH typical of the groundwaters studied), and slightly supersaturated with respect to 

calcite, are calculated as a function of Eh, which is treated as an independent parameter. 

Coexisting values of As, Cd, Hg, Sb, Se, and Zn were set to plausible average values in NWIS 

groundwaters, as shown in Figure 3.6, recognizing artifacts due to application of insufficiently 

sensitive analytical methods. The figure is a vertical section through an Eh–pH, or Pourbaix 

diagram, at a specified pH. Where Eh < -0.23 V, the Pb concentration is defined by the 

coexistence of pyrite and galena. The Pb concentration remains essentially invariant, because 

most of the available S(VI) in solution is reduced to S(-II). At Eh ~ -0.23 V, pyrite becomes 

unstable and decomposes to goethite, at the invariant point where galena, goethite, and pyrite 

coexist. Between Eh ~ -0.23 and ~ -0.18 V, galena and goethite coexist, and the Pb concentration 

increases steeply until saturation is achieved with respect to cerussite, and galena becomes 

unstable. At Eh ~ -0.18 V, cerussite, galena and goethite coexist, whereas at higher Eh, the Pb 

concentration is defined by cerussite alone in the presence of goethite. Because no further redox 

reactions are involved in the system under consideration, the Pb concentration thereafter remains 

essentially invariant with increasing Eh. 

 
 

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Fig. 3.43. Calculated Pb Concentration in Groundwater as a Function of Eh and saturated with respect to Galena 

and Cerussite in the Presence of Goethite and Pyrite. Cerussite = cer; galena = gal; goe = goethite; py = 

pyrite. 

Also included in Figure 3.43 are the minimum detection limits for the analytical methods PLM43 

and PLM48 at 0.0001and 0.00006 mg/L, respectively. These two methods are currently the most 

sensitive used routinely in the measurement of Pb in groundwaters. Pb was detected in nearly 

50% of the analyses using method PLM43 and in over 80% using the more sensitive method 

PLM48. 

The solubility product of galena, K(gal), is defined by the dissolution reaction: 

PbS + H+

 = Pb+2 + HSValues reported in the literature for logK(gal) vary over two orders of magnitude, from -12.78 to 

–14.85, the highest value being based on an experimental determination of galena solubility, and 

the lowest value, currently specified in Data0.dat of EQ3/6 (Appendices A and D), being based 

on thermochemical properties of galena. This large variation in reported values would have had a 

pronounced impact in determining whether the presence of Pb in solution could have been 

detected were galena to coexist with pyrite. However, detailed investigations summarized in 
 

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Appendices D and E revealed the underlying cause for the discrepancies in LogK(gal), and 

allowed reconciliation of experimental determinations of galena solubility with those predicted 

using thermochemical determinations of ΔGf ,Pr ,Tr

0 (galena). A major factor in achieving 

reconciliation came with the recognition that the saturated concentration of Pb in solution is 

determined not only by the presence of known monomeric complexes, but also by the presence 

of previously unrecognized oligomeric sulfide and selenide complexes. These additional 

complexes contributed significantly to elevating the concentration of Pb to such an extent that it 

should be just detectable using the most sensitive analytical methods. Furthermore, it became 

clear that Pb concentrations in equilibrium with galena depend not only on the ambient activity 

of HS-

, but also on that of HSe-, the latter being dependent on saturation with respect to some 

other coexisting solid selenide mineral such as cadmoselite, clausthalite, or tiemannite, for 

example. 

Figure 3.44 shows the calculated frequency of SI’s for galena in groundwaters analyzed for Pb 

by analytical methods PLM43 and PLM48, while assuming that logK(gal) = –14.85 (Appendix 

D, Table D2) and that Eh is specified either by (a) the Fe(III)/Fe(II) couple and goethite 

saturation, or (b) by the goethite/pyrite couple. The two representations illustrate, respectively, 

the minimum and maximum likely calculated frequencies of galena saturation. Both histograms 

indicate that SI(max) ~ +0.5, suggesting galena saturation in the subset of NWIS groundwaters 

examined. It should be noted that all thermodynamic calculations are made assuming that the 

temperature of the groundwater is 25oC. In fact, most shallow groundwaters have temperatures 

closer to 10oC, and correction for temperature would result in a correction to logK(gal) of 

approximately –1.0. Given that the uncertainties associated with the evaluation could be as high 

as ±1.0 in SI, overall agreement is excellent. 
 

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(a) (b) 

Fig. 3.44. Frequency Distribution of Saturation Indices of Galena in Groundwater Samples analyzed for Pb using 

PLM43 and PLM48. (a) Eh is defined by Fe(III)/Fe(II) in solution in the presence of goethite. (b) Eh 

is defined by the coexistence of pyrite and goethite. 

Because of the role that Se plays in complexing Pb in solution, and the fact that the 

corresponding selenide analogue of galena might control the Pb concentration, SI histograms for 

clausthalite (Pb selenide) were also calculated using the same analytical data for Pb as used in 

the calculation of the corresponding galena histograms. These are illustrated in Figure 3.45. Both 

histograms indicate that SI(max) ~ -1. When compared with galena (Figure 3.44), these findings 

suggest that clausthalite is undersaturated in the suite of groundwaters examined. However, the 

overall uncertainties in both thermochemical data for clausthalite, estimates of sulfide and 

selenide Pb complexation constants, and other factors relating to the groundwater quality 

analyses preclude making such an assertion with confidence. 

The frequency distribution of SI’s for pyrite in the selected groundwater samples used in the 

construction of Figures 3.44 and 3.45 is presented in Figure 3.46a (where Eh is again defined by 

Fe(III)/Fe(II) in solution in the presence of goethite). Only a small fraction of samples are 

saturated with respect to pyrite, and as noted above, it is not clear whether these results are 

meaningful. Figure 3.46b also shows that most of the groundwaters analyzed for Pb by PLM43 
 

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and PLM48 are saturated with respect to calcite, consistent with the groundwater composition 

used to calculate the Pb concentration in Figure 3.43. 

(a) (b) 

Fig. 3.45. Frequency Distribution of Saturation Indices of Clausthalite in Groundwater Samples analyzed for Pb 

using PLM43 and PLM48. (a) Eh is defined by Fe(III)/Fe(II) in solution in the presence of goethite. 

(b) Eh is defined by the coexistence of pyrite and goethite. 

(a) (b) 

Fig. 3.46. Frequency Distribution of Saturation Indices of (a) Pyrite and (b) Calcite in Groundwater Samples 

analyzed for Pb using PLM43 and PLM48. The Saturation Indices are calculated assuming Eh to be 

defined by Fe(III)/Fe(II) in solution in the presence of goethite. 
 

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Cadmium. Figure 3.47 illustrates the variation in cadmium concentration in solution as a 

function of Eh for the same groundwater, with pH = 7.6, as used in the analysis of Pb solubility 

in the preceding section. Below Eh ~ -0.22 V, CdS (greenockite) coexists with pyrite. Between 

Eh ~ -0.22 and ~ -0.17 V, CdS coexists with goethite. Above Eh ~ -0.17 V, CdS decomposes, 

and Cd concentration is controlled by otavite (CdCO3), and is independent of Eh. It should be 

noted that the leveling off of Cd concentration at Eh < -0.22 V is due to the conversion of all 

available S(VI) in solution into S(-II) species, i.e., HS-

 and H2S(aq). The saturation control of 

Cd in solution by otavite has been shown to occur under field conditions, as demonstrated by 

Carroll et al. (1998) and McGrath et al. (2007). White et al. (2004) have also shown that Cd is 

readily leached from soils by dilute acid treatment, suggesting the presence of Cd as a carbonate. 

This is in contrast to the leaching behavior of Zn, which suggests stabilization as a silicate (see 

below). 

As noted in Appendix B2, most of the reported analyses for Cd in groundwaters appear to be 

artifacts, due to insufficient sensitivity of past analytical methods. The detection limit by the 

most sensitive analytical methods used routinely in the analysis of Cd in groundwaters, PLM43 

and PLM47, is 0.00004 mg/L, as included in Figure 3.47. Only 24% of the analyses using 

PLM43 and 17% of the analyses using PLM47 detected Cd in solution. 

Independent determinations of greenockite solubility logK(CdS), represented by the reaction 

CdS + H+

 = Cd+2 + HShave been reported in the literature, as noted in Appendix D, and differ by nearly two orders of 

magnitude, i.e., logK(CDS) = -14.36 and –15.91, respectively. The smaller value is currently 

specified in Data0.dat of EQ3/6. As noted above, the discrepancies were reconciled as described 

in Appendices D and E, and a LogK(CdS) was chosen based on a thermochemical determination 

of ΔGf ,Pr ,Tr

0 (CdS). If CdS were to coexist with pyrite, Cd in solution would be about one order of 

magnitude above the detection limit of PLM43 and PLM47 at 0.00004 mg/L. In the absence of 

pyrite, Cd concentrations in equilibrium with CdS should be even more easily detectable as 

saturation with respect to otavite is approached. A few of the Cd analyses fall within the range of 
 

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otavite solubility, i.e., circa Log Cd ~ -1.5 (mg/L), as shown in Figure 3.6d, and probably 

represent conditions in oxidized aquifers, in analogy with cerussite saturation with respect to Pb 

(Figure 3.43). The calculated solubility is therefore about one order of magnitude greater than the 

apparent marginal detectability of Cd in solution, but correction for lower ambient temperatures 

than the 25oC reference and other factors could easily allow for reconciliation of this 

discrepancy. 

Fig. 3.47. Calculated Cd Concentration in Groundwater as a Function of Eh and saturated with respect to CdS 

and Otavite in the Presence of either Pyrite or Goethite. CdS = CdS; goe = goethite; ota = otavite; py = 

pyrite. 

Figure 3.48 illustrates the SI histograms for CdS where Eh is defined either by Fe(III)/Fe(II) in 

the presence of goethite (Figure 3.48a), or by the coexistence of goethite and pyrite (Figure 

3.48b). Both figures show SI(max) ~ 0, indicating saturation. However, Figure 3.48b also 

displays a bimodal distribution with a secondary peak at SI(max) ~ -3. An explanation for this 

secondary peak is not readily available. It cannot be caused by an insufficiently sensitive 

analytical method, because the histogram is based on only those analyses for Cd using PLM43 

and PLM47, both of which have the same detection limits. A more likely explanation is that the 

two peaks arise from the omission of Se analyses from those samples comprising the peak where 
 

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SI(max) ~ 0. In this case, the lower peak, where selenide complexing is taken into account, 

would indicate that CdS must be a component of either sphalerite or galena in solid solution. 

However, this would imply that CdS saturation would be about two log units higher than 

calculated in Figure 3.48, in which selenide complexation is accounted for. 

SI frequency histograms for cadmoselite are given in Figure 3.49. Both indicate SI(max) ~ 0, and 

the results are relatively unambiguous. Furthermore, the population of analyses permitting 

calculation of SI(cadmoselite) in Figure 3.49b is approximately equivalent to that populating the 

secondary peak in Figure 3.48b. From this comparison, one is drawn to the tentative postulate 

that, with respect to the NWIS groundwaters studied and analyzed for Cd using PLM43 and 

PLM47, CdS is unsaturated in solution, and that the phase controlling Cd solubility is 

cadmoselite. Clearly, further review of the composition of the affected groundwater analyses, 

thermodynamic data refinement, and additional corroboration is required before this finding can 

be accepted. 

(a) (b) 

Fig. 3.48. Frequency Distribution of Saturation Indices of CdS in Groundwater Samples analyzed for Cd using 

PLM43 and PLM47. (a) Eh is defined by Fe(III)/Fe(II) in solution in the presence of goethite. (b) Eh 

is defined by the coexistence of pyrite and goethite. 
 

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(a) (b) 

Fig. 3.49. Frequency Distribution of Saturation Indices of Cadmoselite in Groundwater Samples analyzed for Cd 

using PLM43 and PLM47. (a) Eh is defined by Fe(III)/Fe(II) in solution in the presence of goethite. 

(b) Eh is defined by the coexistence of pyrite and goethite. 

Zinc. It is instructive to compare the behavior of Zn in solution with that of Cd under similar 

conditions, since their sulfides are crystallographically similar, i.e., sphalerite is the structural 

analogue of hawleyite, and wurtzite is the analogue of greenockite (CdS). However, in contrast 

to Cd, the detection and quantification of Zn in groundwaters is readily achieved with current 

analytical methods (Figure 3.6i). For example, although the minimum detection limit using the 

most sensitive analytical method routinely available for Zn, PLM43, is higher at 0.0006 mg/L 

compared to 0.00004 mg/L for Cd, Zn was detected in 94% of the samples analyzed, in contrast 

with only 24% for Cd. Furthermore, Figure 3.6i does not show any obvious indications of 

analytical artifacts, so clearly suggested by the corresponding Figure 3.6d for Cd. 

Figure 3.50 shows the calculated Zn concentration as a function of Eh in the same groundwater 

as that for Cd in Figure 3.47. The coexistence of sphalerite with pyrite and goethite occurs at a 

Zn concentration of approximately 0.002 mg/L, which is in contrast with the corresponding 

three-phase coexistence where Cd concentration is 0.0002 mg/L. Under oxidizing conditions, 

sphalerite is transformed to hemimorphite (Zn4Si2O7(OH)2.(H2O)) (McPhail et al., 2006). That a 
 

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silicate such as hemimorphite is likely to immobilize Zn is substantiated by leaching studies on 

soils distal to sulfide mineralization in Australia, where no more than 50% Zn is mobilized by 

acid leaching of the <65μm fraction, but Hf/aqua regia is required to solubilize the remainder 

(White et al., 2004). This is in contrast to Cd, where almost all in the <65 μm fraction was 

mobilized by acid leaching alone. The solubility product for hemimorphite at 25oC was retrieved 

from preliminary data presented by McPhail et al. (2003), and utilized in the construction of 

Figure 3.50. The limitation of the minimum Zn concentration to approximately log Zn = -10 is 

due also to the conversion of all S(VI) to S(-II) species. The concentration of Zn in solution 

where goethite, sphalerite, and hemimorphite coexist is calculated to be 0.004 mg/L. 

As noted in Appendix D, two independent determinations of sphalerite solubility logK(sph), 

represented by the reaction (ZnS + H+

 = Zn+2 + HS-) are in substantial agreement, i.e., logK(sph) 

= -11.44 and 11.46, respectively, the higher value being currently specified in Data0.dat of 

EQ3/6. The value derived from thermochemical measurements was used to calculate LogK(sph), 

and the potential participation of neutral oligomeric neutral sulfide and selenide complexes was 

taken into account as described in Appendices D and E. 

The SI frequency histogram for sphalerite in Figure 3.33a gives SI(max) = +1, where Eh is 

calculated on the basis of Fe(III)/Fe(II) ratio in a solution saturated with respect to goethite. 

Figure 3.42a shows a corresponding histogram giving SI(max) = +1, where Eh is calculated 

assuming a goethite/pyrite buffer. The maximum frequency of Zn concentration is circa Log Zn 

= -1.5 (mg/L), which is about one log unit higher than the calculated concentrations illustrated in 

Figure 3.50 for both sphalerite and hemimorphite, and is consistent with the SI frequency 

diagrams cited above. This discrepancy suggests that either the predicted ΔGf ,Pr ,Tr

0 (sph) and Log 

K(hemi) have imposed excessive stability on these minerals in solution, or, in the case of 

sphalerite, that the stabilities of the sulfide and selenide complexes are greater than predicted on 

the basis of the evaluations given in Appendices D and E. Because of the complexity of the 

system and uncertainties associated with the thermodynamic parameters used, together with 

significant variations in water quality, a more rigorous evaluation will be needed than that 

presented in this scoping study. Given the cumulative uncertainties inherent in this analysis, the 
 

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close correspondence between predicted and observed degrees of saturation with respect to 

sphalerite and hemimorphite is very good. 

Fig. 3.50. Calculated Zn Concentration in Groundwater as a function of Eh and Saturated with respect to either 

Sphalerite or Hemimorphite in the Presence of Goethite and Pyrite. goe = goethite; py = pyrite; sph = 

sphalerite; hemi = hemimorphite. 

In conclusion, the theoretical calculations provide a basis for explaining field observations and 

substantiate the proposition that sphalerite controls Zn concentrations in groundwaters under 

moderate to strongly reducing conditions, and hemimorphite under mildly reducing to oxidizing 

conditions. 

3.4.7 Summary of Thermodynamic Controls 

Conclusions as to which minerals thermodynamically control the activities of the selected 

hazardous constituents in NWIS potable groundwaters in the United States are summarized in 

Table 3.13. Most groundwaters, at least 75% of all samples from the NWIS data base, are 

moderately to strongly reducing, with the lower bound to the redox state most likely buffered by 

pyrite, or other ferrous sulfide, and another mineral containing iron in either the Fe(II) or Fe(III) 
 

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state. A small fraction of groundwaters, estimated to be perhaps as low as 5 percent, may be 

mildly reducing or even oxidizing, as suggested by the behavior of certain hazardous constituents 

such as As, Cd and Pb that they might. Table 3.13 recognizes the distinction between these two 

categories of groundwater, as defined broadly by their redox states. 

The claims presented in Table 3.13 are generalizations that apply to potable groundwaters of 

average composition and pH. They are used to construct a geochemical model of general 

applicability for addressing problems related to groundwater contamination, as demonstrated in 

Section 4 of this report. Overall model uncertainty is difficult to estimate; rigorous site-specific 

characterization would be necessary to further evaluate model uncertainty. Model uncertainties 

are evaluated in greater detail in the following Section 3.5. The selection of water quality data 

meeting more stringent criteria, a more critical review of thermodynamic data already used in 

geochemical model analysis, and incorporation of additional aqueous species relevant to the 

modeling effort could limit model uncertainty in future studies. 

Table 3.13. Summary of minerals identified as controlling the thermodynamic activity of selected hazardous 

constituents in potable groundwaters 

Con- Thermodynamic Control on Groundwater Activity 

stituent Moderately - Strongly 

Reducing, i.e., 

Pyrite/Goethite Buffer 

Mildly Reducing - 

Oxidizing 

Comments 

As (1) Arsenopyrite in 

solid solution in pyrite 

(arsenian pyrite) 

(2) Arsenopyrite 

Adsorption on host 

rock mineral 

surfaces, especially 

iron oxides, or 

through substitution 

for SO4

-2 in anionic 

clays such as green 

rust 

No mineral in which As is an essential component, other 

than arsenopyrite, has been identified controlling As 

activity in solution in the groundwaters examined. 

Ba Barite Barite Barium activity is controlled by barite alone, regardless 

of oxidation state. No other Ba minerals controlling Ba 

activity have been identified. 
 

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Con- Thermodynamic Control on Groundwater Activity 

stituent Moderately - Strongly 

Reducing, i.e., 

Pyrite/Goethite Buffer 

Mildly Reducing - 

Oxidizing 

Comments 

Cd (1) CdS (greenockite) 

substitution in either 

galena or sphalerite 

(2) Cadmoselite 

Otavite Further evaluation is required to resolve ambiguities. 

Greenockite could saturate at potable water aquifer 

temperatures, as solid solution substitution in sphalerite 

and/or galena decreases with falling temperature. The 

observed saturation with respect to cadmoselite suggests 

that this phase may control Cd+2 activity in the evaluated 

NWIS groundwaters for which Cd and Se analyses are 

available. Equilibrium concentrations in solution are so 

low, however, that current analytical methods are 

marginally effective in providing quantitative 

measurements. Under oxidizing conditions, cadmium 

activity is controlled by otavite. Otavite cannot be 

present in the majority of potable groundwaters analyzed 

for Cd in the NWIS database, because Cd concentrations 

would be easily detected by current state-of-the art 

analytical methods. This observation indirectly supports 

the conclusion that most groundwaters are reducing. 

Hg (1) HgS solid solution 

in pyrite 

(2) Cinnabar 

(3) Tiemannite 

Native Mercury 

Pb (1) Galena 

(2) Clausthalite 

Cerussite A small fraction, ≈ 5%, of groundwaters is sufficiently 

oxidizing that Pb activity is defined with respect to 

cerussite saturation. This observation indirectly supports 

the conclusion that most groundwaters are reducing. Pb 

mineralogy under mildly reducing – oxidizing conditions 

is complex, and it is possible that secondary Pb silicates 

might also control Pb activity in groundwaters under 

these conditions. 

Sb (1) Gudmundite solid 

solution in pyrite. 

(2) Kermesite (red 

antimony) 

Kermesite (red 

antimony). 

Se (1) Cadmoselite 

(2) Tiemannite 

(3) Clausthalite 

(4) Dzharkenite solid 

solution in pyrite 

None recognized Solid solution substitution of the dzharkenite (FeSe2) 

component in pyrite, when present, could also be 

responsible for defining Se(-II) activity. No potential 

selenite of selenate minerals are recognized as activity 

controlling under mildly reducing - oxidizing conditions. 

U (1) Uraninite and/or (2) 

Coffinite 

None recognized Uranophane is strongly undersaturated in all cases when 

the redox state of the groundwater was specified by the 

goethite-Fe(III)/Fe(II) Eh buffer at a given pH. 

Zn Sphalerite Hemimorphite 
 

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3.5 The Variation of Hazardous Constituent Concentrations with 

Partial Pressure of Carbon Dioxide in Groundwaters 

3.5.1 Introduction 

Given certain assumptions and approximations, changes in the groundwater equilibrium 

concentrations of hazardous inorganic constituents caused by changes in the partial pressure of 

CO2 can be calculated using a distribution-of-species code. In this section, we present such 

calculations, performed with the distribution-of-species simulator EQ3/6, and discuss their 

significance and limitations. The equilibrium calculations reflect the direct calibration of the 

model to the pertinent field data for the hazardous constituents and give preliminary estimates of 

the potential magnitude of the impact of CO2 intrusion on groundwater quality. Further, the 

results indicate which hazardous constituents are sensitive to CO2 intrusion and which are not. 

In the following subsections, we first discuss the inputs to the thermodynamic model (Section 

3.5.2) and then present the base case results from the equilibrium calculations (Section 3.5.3). In 

Section 3.5.4, we evaluate the overall uncertainties associated with the model and the chemical 

analyses, and show the impact that such uncertainties may have on the results. Finally, we 

examine how aqueous complexation impacts the total concentrations of each constituent in 

groundwaters with changing P(CO2) (Section 3.5.5). 

3.5.2 Input Specifications 

Before providing further details regarding this thermodynamic analysis, it is important to 

understand the relationship between (1) the thermodynamic model of the geochemistry of 

groundwaters used to predict equilibrium concentrations of given hazardous constituents under 

specified equilibrated CO2 partial pressures, and (2) the observed concentrations of these same 

constituents in NWIS groundwaters. The thermodynamic model was developed entirely 

independently of any of the chemical analyses of NWIS groundwaters. It was based on the 

existing thermochemical database in EQ3/6, which was revised and augmented using recent 

literature on calorimetrically or experimentally derived solubility products (Section 3.4.2; 
 

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Appendices C, D, and E). This independent thermodynamic model was then used in Section 

3.4.5 to evaluate the chemical analyses and to determine to what extent the groundwaters were 

saturated with respect to minerals that might control the activities of the selected hazardous 

constituents. The candidate minerals are present at such low abundances, however, that their 

direct detection is practically impossible without using advanced spectroscopic techniques. 

Therefore, with rare exceptions, we can only surmise their presence through thermodynamic 

evaluation. 

In the evaluation of solubility controls for hazardous constituents presented in Section 3.4.5, the 

modes of the histograms of the logarithm of the SI of each candidate mineral are determined. 

The calculated mode differs from the ideal value of zero by ± some incremental value, reflecting 

a “closing error” between model prediction and actual field observation. This deviation is a 

measure of the overall uncertainty of all parameters that go towards construction of the model, 

including the uncertainties and variabilities associated with the 38,000 chemical analyses of the 

NWIS groundwaters as well as uncertainty as to whether the candidate mineral is a plausible 

thermodynamic control. It is desirable to ensure that the calculated model variations in hazardous 

constituent concentrations as a function of P(CO2) are consistent with the field observations. This 

was done here by adjusting in the model the Log K values of the dissolution constants of the 

minerals plausible mineral candidates so that the calculated log SI’s of the mode for each are 

now zero, rather than showing a closing error. In other words, the model is now calibrated to the 

selected modes of the histograms of the SI’s calculated from NWIS groundwater analyses. 

Table 3.14 summarizes the imposed input constraints for the base case runs, including the 

modifications made in the calibration. The list of minerals identified as controlling hazardous 

constituent concentrations is based on the thermodynamic evaluations summarized in Section 

3.4.7. Thermochemical data, including the measured solubilities of most of these minerals, are 

available (Appendix C, D, and E), and calorimetrically or experimentally derived solubility 

products at 25°C were entered in the EQ3/6 Data0.dat database where needed. Calculations were 

performed using a typical groundwater composition. The redox state was defined by the assumed 

equilibrium between pyrite and goethite at a given pH to simulate reducing conditions typical of 
 

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shallow confined aquifers. The groundwater was also assumed to be saturated with respect to 

calcite, opal-CT, and low Fe, Mg smectite. (The effect of these minerals on the solubilities of the 

hazardous inorganic constituents is negligible.) Electrical neutrality was achieved by adjustment 

of pH. Also, as mentioned before, previously unrecognized metal sulfide and selenide 

complexation in solution is taken into account in the Data0.dat database. 

Calibration adjustments to the log K values of the solubility products of minerals that control the 

concentrations of Pb, Hg, Se, U and Zn in NWIS groundwaters were made to cinnabar, galena, 

and tiemannite, uraninite and sphalerite, respectively, as indicated in Column 4 of the Table 3.14, 

the purpose being to ensure consistency between the modal concentrations of these constituents 

and the thermodynamic model. Corrections were also made to the solubility products of 

arsenopyrite and gudmundite, to reflect the assumption that both As and Sb are substituting in 

solid solution in pyrite as FeAsS and FeSbS components. The magnitude of each correction is 

based on the calculated mode of the SI distribution determined through the analysis of NWIS 

groundwaters given in Section 3.4.5.2. No adjustments were made to the log K values of barite 

or CdS (greenockite). In the former, there appears to be substantial agreement between model 

predictions and observed concentrations of Ba. Regarding the latter, considerable ambiguity 

remains regarding the saturation state in NWIS groundwaters, and calibration was not attempted. 

3.5.3 Results from Equilibrium Calculations 

Results from the calculations of the saturation concentrations for each hazardous constituent as a 

function of P(CO2) are presented graphically in Figure 3.51. The figure shows the most likely 

expected concentrations (black lines) as well as a prediction of the uncertainty band (shaded 

areas) based on the uncertainty analsyis described in Section 3.5.4. Also included in the figure 

are the relevant MCLs (or SWDL for Zn), the detection limits for the most sensitive analytical 

methods, and the estimated ±2σ variation about the mean of the log concentration of each 

hazardous constituent at log P(CO2) = -2, i.e., the average natural partial pressure of CO2 in 

NWIS groundwaters (see Figure 3.17). The estimated ±2σ variation about the mean is assumed 

to be ≈ ±1.5 log(mg/L), and is based on the observed log-normal distributions of Ba, U and Zn in 
 

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NWIS potable groundwaters, all of which gave quantitative measurements for ≥90% of all 

samples (see Table 3.17 below). For Pb and Se, the lower “tail” of the distribution falls below the 

limits of detection for the most sensitive analytical methods used, whereas for Cd. Hg and Sb, 

the estimated mean value falls below the corresponding most sensitive detection limits. For Cd, 

Hg, and Sb, therefore, the variation in natural groundwaters was estimated assuming that the 

percentage of detectable values represents an observed upper “tail” of the log-normal 

distribution. 

We shall start discussing the general trends observed for most likely results shown as black lines 

in Figure 3.51. The concentrations of some of the hazardous constituents are quite sensitive to 

changes in P(CO2), whereas others remain relatively unaffected. For example, Hg, Se, and U are 

insensitive to changes. Ba is slightly affected, but the maximum concentration at elevated 

P(CO2) (~ 1.1 mg/L at 10 bar) remains below the MCL (maximum contaminant level) of 2 mg/L. 

Cd, Pb, and Zn show marked increases after log P(CO2) rises above -1. However, only in the 

case of lead is the maximum concentration at elevated P(CO2) (~ 0.02 mg/L at 10 bar) higher 

than the MCL of 0.015 mg/L. Arsenic concentration changes sympathetically with increasing 

P(CO2), with a maximum concentration (~ 0.46 at 10 bar) significantly higher than the MCL of 

0.01 mg/L. Sb initially increases in parallel with As, then saturates with respect to kermesite at 

Log P(CO2) > -2, and thereafter remains at a concentration level significantly below the MCL of 

0.006 mg/L. The reasons for these differing behaviors can be understood by reference to 

variations in pH and Eh, and to variations in the nature and distributions of complexes of each 

element in aqueous solution. These variations are discussed further in Section 3.5.5. 
 

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Table 3.14. Input specifications for base case EQ3 runs to calculate the equilibrium concentrations of hazardous 

inorganic constituents in groundwater at 25°C as a function of P(CO2). SS = Solid Solution. 

Adjustments2 Aqueous Basis 

Species 

Concentration 

(mg/L) 

Mineral 

Saturation1 Specified Change Explanation 

H+ - - Charge balance Electrical neutrality 

Na+ 45.8 - 

K+ 5.04 - 

Mg+2 2.01 - 

Ca+2 Set by mineral 

saturation 

calcite 

Fe+2 Set by mineral 

saturation 

pyrite 

Al+3 Set by mineral 

saturation 

low Fe,Mg 

smectite 

 

SiO2(aq) Set by mineral 

saturation 

opal-CT 

O2(g) Set by mineral 

saturation 

goethite 

HCO3

-

 Set by P(CO2) 

saturation 

log P(CO2) -4, –3, -2, -1, 0, +1 Variation from background 

due to CO2 intrusion 

Cl- 7.14 - 

SO4

-2 18.4 - 

H2AsO4

-

 Set by mineral 

saturation 

arsenopyrite SS Change Log K from 

+58.66 by -3.00 to 

+55.66 

Calibration. Change also 

accounts for solid solution 

Ba+2 Set by mineral 

saturation 

barite Log K = -9.97 (no 

change) 

No calibration needed 

Cd+2 Set by mineral 

saturation 

CdS (greenockite) Log K = -14.09 (no 

change) 

No calibration needed 

Hg+2 Set by mineral 

saturation 

cinnabar Change Log K from 

-38.08 to -37.50 

Calibration 

Pb+2 Set by mineral 

saturation 

galena Change Log K from 

-14.87 to -14.40 

Calibration 

Sb(OH)3(aq) Set by mineral 

saturation 

gudmundite SS, 

kermesite 

Change Log K from 

+38.76 by -5.00 to 

+33.76 

Calibration. Change also 

accounts for solid solution 

SeO3

-2 Set by mineral 

saturation 

tiemannite Change Log K from 

-43.27 to -44.10 

Calibration 

UO2

+2 Set by mineral 

saturation 

uraninite Change Log K from 

-4.83 to –3.83 

Calibration 

Zn+2 Set by mineral 

saturation 

sphalerite Change Log K from 

-11.27 to -10.00 

Calibration 

1. Mineral saturation is specified, which determines the aqueous concentration of the basis species identified in 

column 1 through the law of mass action. 

2. Adjustments specified in the input file of EQ3. All adjustments with the exception of H+ and HCO3

-

 involve 

corrections to Log K values of mineral solubility products in order to calibrate the thermodynamic geochemical 

model with the mode of the corresponding mineral saturation indices in the sampled NWIS groundwaters. 

Charge balancing is assumed through adjustment of H+

 by the EQ3 code algorithm. HCO3

- is specified through 

successive incremental adjustment of log P(CO2) to explore the effect on hazardous constituent concentrations 

of the full range of P(CO2) anticipated in the subsurface environment.
 

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Figure 3.51 Part 1. 
 

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Fig. 3.51. Variation of trace metal concentrations in equilibrium with host minerals as a function of CO2 partial 

pressure taking into account both sulfide and selenide complexation, and with solubility products 

calculated using thermochemical data and adjusted to ensure consistency with hazardous constituent 

concentrations in NWIS waters (see Table 3.14). (SS) refers to solid solution. CV013, CV014, CV016, 

XV017, CV018, FL007, PH0S3, PLM10, PLM40, PLM43, PLM47 and PLM48 are analytical methods 

cited in the NWIS database. See Table A2 in Appendix A. 
 

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3.5.4 Uncertainties Associated with Equilibrium Calculations 

3.5.4.1 Estimation of Aggregate Uncertainties 

The overall uncertainty related to predictions of hazardous constituent concentrations in potable 

groundwaters as a function of P(CO2) is difficult to estimate, as so many factors affect the 

analysis and interpretation. Briefly, four components are involved: (1) the validity of 

assumptions relating to the geochemical model, (2) uncertainties in geochemical model 

parameters, (3) uncertainties relating to the chemical analyses of NWIS groundwaters, and (4) 

uncertainties relating to the natural variability in sampled NWIS groundwater compositions. 

Tables 3.15-3.18 list the various uncertainties under these four categories, and their potential 

impact on hazardous constituent behavior is discussed. 

The treatment of uncertainty in this section is at best semi-quantitative. Most of the listed 

estimates of uncertainty are based on professional judgment, and not on quantitative evaluation, 

the reason being that it would otherwise take an inordinate amount of time and effort to 

accomplish such a task, even if technically feasible. The term “uncertainty” is assumed to be the 

standard deviation of the mean, i.e., ±σ, except where otherwise noted. The listed uncertainties in 

Table 3.15-3.17 are then selectively used to calculate the aggregate uncertainties associated with 

the calibrated log K values of the solubility products of the hazardous constituent host minerals. 

This information is summarized in Table 3.19. 

Uncertainties listed in Table 3.18 are not utilized in the calculation of aggregate uncertainties, 

because these natural-variability uncertainties are accounted for in the estimated ±2σ variation 

observed in hazardous element concentrations at the modal logP(CO2) ≈ -2 . Furthermore, it is 

the purpose of the analysis to compare the aggregate geochemical model uncertainty in 

calculated hazardous concentrations with the observed apparent natural variability in order to 

obtain some measure as to whether or not the natural variability exceeds modeled uncertainty.

 
 

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Table 3.15. Uncertainties associated with geochemical model assumptions 

No. Item Principal Concern Impact on Findings Justification for Current 

Assumptions 

1 Specified mineral 

hosts control 

hazardous 

constituent 

concentrations. 

Identification of mineral hosts 

was made through indirect 

thermodynamic comparison of 

predicted mineral solubilities 

with those in 38,000 potable 

groundwaters. The presence of 

mineral hosts in the subject 

aquifers has not been verified. 

If selected minerals have been 

incorrectly identified, then the 

predicted response to 

changing P(CO2) could differ 

markedly, and the present 

interpretation would be 

invalid 

The correlation between 

predicted solubilities and the 

modes of the compositions for 

all selected constituents in 

38,000 groundwaters is 

consistent with 

thermodynamic control by the 

selected minerals. 

2 Mineral hosts are 

pure substances, 

except where 

otherwise noted. 

In the case of Cd, Sb and Se, it 

is not entirely clear whether 

pure substances or solid 

solutions control these 

constituent concentrations. 

If the hazardous constituent 

were a minor component in 

solid solution in a mineral 

other than that identified, the 

thermodynamic response to 

P(CO2) changes would differ 

from that in the present 

model. 

Identified pure substances 

containing Cd, Sb and Se, 

where relevant, are consistent 

with observed concentrations 

in groundwaters. Both field 

observations and 

thermodynamic analyses are 

consistent with the assumption 

that As is present in solid 

solution in pyrite. 

3 Oligomeric sulfide 

and selenide 

complexes of Cd, 

Hg, Pb, and Zn are 

present in solution 

and impact 

equilibrium 

concentrations of 

these constituents. 

The exact compositions of the 

S and Se oligomers have not 

been determined. Selenide 

speciation is assumed to be 

analogous to that of sulfide 

speciation, based on correlation 

between Hg-S(-II) and Se(-II) 

species. 

If incorrect, both the 

concentrations and the 

thermodynamic response to 

changes in P(CO2) would 

differ from that in the present 

model for the listed species 

and Se. 

The assumption that 

oligomeric sulfide and 

selenide complexes of Cd, Hg, 

Pb, and Zn are present in 

solution, and that the 

thermodynamic properties of 

selenide complexes correlate 

with those of sulfide 

complexes is necessary to 

achieve consistency between 

model predictions of 

hazardous constituent 

concentrations and field 

observations. 

4 The pyrite/goethite 

pair buffers Eh at a 

given pH. 

Goethite is metastable with 

respect to hematite, i.e., 

pyrite/hematite might be 

operative in some aquifers. 

Other buffers might be 

operative, e.g., 

pyrite/magnetite, 

pyrite/siderite, pyrite/chlorite, 

etc. A systematic study as to 

the ranges of conditions under 

which these alternative buffers 

might operate, and how they 

would impact results has not 

been conducted. It is further 

assumed, but not proven, that 

the Eh buffer is in 

thermodynamic equilibrium 

with the aqueous phase. 

Variations in Eh at a given pH 

due to control by alternative 

buffers could impact mineral 

host solubilities, possibly by 

±1 to 2 log units. 

Most of the alternative Eh –

pH buffers control Eh at a 

given pH in a range that lies 

within the overall uncertainty 

of the calculations. 

Groundwater analyses are 

generally consistent with the 

assumption that the 

pyrite/goethite buffer closely 

represents conditions in most 

potable water aquifers. An 

alternative assumption that Eh 

was defined by goethite 

saturation in the presence of 

measured Fe+2 concentration 

at a given pH did not yield 

results consistent with 

hazardous constituent 

concentrations saturated with 

respect to the specified host 

mineral hosts. 
 

146 of 352

No. Item Principal Concern Impact on Findings Justification for Current 

Assumptions 

5 Reversible 

chemical 

equilibrium is 

assumed with 

respect to selected 

mineral hosts. 

Some geochemical systems, 

especially those involving 

redox reactions, are notoriously 

slow at achieving chemical 

equilibrium (see No. 4, above). 

This is especially pertinent 

where reactions involve 

transformations between S(-II) 

and S(VI). 

If partial oxidation in the field 

leads to dissolution of sulfides 

of Cd, Hg, Pb, and Zn with 

formation of native sulfur or 

other intermediate species 

instead of SO4

-2, the stability 

fields of these sulfides would 

be extended to higher 

oxidation states. This would 

in turn lead to a broader 

distribution of observed 

concentrations. 

The statistical dispersion of 

the SIs of the identified 

mineral hosts is suggestive 

that not all groundwaters are 

in thermodynamic 

equilibrium. The agreement 

between predicted and modal 

values with respect to redoxsensitive mineral hosts 

indicate, however, that 

substantial equilibrium has 

been achieved in many 

groundwaters under buffered 

redox (i.e., pyrite/goethite) 

conditions. 

6 Temperature and 

pressure is assumed 

to be 25oC and one 

bar, respectively. 

The temperatures of most 

groundwaters within the 48 

contiguous states fall between 

5oC and 30oC with a mode at 

aroud 12oC (see Figure 3.52). 

Hydrostatic pressures in 

potable water aquifers are 

unlikely to exceed 10 bars. 

Correction of log K for the 

dissolution reaction of a host 

mineral from 25oC to 10oC 

ranges from ≈ –2.0 to ≈ +0.75 

for the host minerals under 

consideration. Pressure 

changes of only one bar 

would have negligible impact 

on thermodynamic equilibria 

and can be ignored. With 

respect to most hazardous 

constituent complexes, the 

values of their dissociation 

constants at other than 25o are 

unavailable, and therefore 

corrections for temperature 

cannot be made at present. 

With the exception of 

uraninite, none of the 

corrections of host mineral 

solubility products for 

temperature would improve 

the correlation between model 

predictions and field 

observations. Such 

corrections are generally 

within the overall uncertainty 

of the model and the field data 

used for comparison. 

 

7 Activity 

coefficients of 

some sulfide and 

selenide complexes 

of hazardous 

constituents in 

supporting 

electrolytes in 

experiments were 

assumed to be 

unity. 

Supporting electrolytes were 

sometimes seawater, or 1 N 

perchlorate solutions (e.g., Hg 

selenide complexes). 

Failure to correct for ionic 

strength in such solutions 

could lead to errors in 

associated dissociation 

constants of as high as ±1 log 

unit. 

Activity coefficient 

corrections, especially for illdefined oligomeric species, 

are difficult to implement. In 

general, corrections to 

calculated dissociation 

constants would be less than 

±0.5 log units, and even less 

for oligomeric neutral species, 

and are therefore within the 

uncertainty of the model 

predictions. 

8 TDS is assumed to 

be zero. 

Corrections for ionic strength 

are not made. 

Errors thereby introduced are 

very small, as most potable 

waters have very low (≤1000 

mg/L) TDS. 

Impact on hazardous 

constituent concentrations is 

negligible. 
 

147 of 352

No. Item Principal Concern Impact on Findings Justification for Current 

Assumptions 

9 All hazardous 

constituent mineral 

hosts are coarsely 

crystalline. 

Some minerals, especially those 

formed as secondary 

precipitates, could be 

amorphous or very finely 

crystalline. Amorphous or 

finely crystalline phases are 

more soluble than their coarsely 

crystalline counterparts. 

Failure to account for 

increased solubility of 

hazardous constituent host 

minerals could lead to 

predictions of hazardous 

constituent concentrations 1 – 

2 orders of magnitude lower 

than actually occurs. 

Hazardous constituent mineral 

hosts are assumed to have 

coarsened through the 

mechanism of Ostwald 

ripening over perhaps millions 

of years, and therefore mineral 

crystallinity or surface free 

energy effects are expected to 

be negligible. 

10 Silica activity in 

solution is 

controlled by 

saturation with 

repect to opal-CT. 

Not all waters are saturated 

with respect to opal-CT; many 

are expected to be saturated 

with respect to quartz. 

Choice of silica mineral 

affects predictions of coffinite 

saturation. 

The assumption that silica is 

saturated with respect to opalCT in shallow aquifers is 

reasonable, and consistent 

with field observations of such 

systems undergoing 

diagenesis. 

11 Aluminum in 

solution is 

controlled by 

saturation with 

repect to low 

Fe,Mg smectite. 

Aluminium concentration is 

under-predicted when 

compared with field 

observations. 

In general, the impact of 

incorrectly simulating Al 

concentrations on predictions 

of the concentrations of 

hazardous constituents is 

small. 

Because the underlying 

problem concerning Al 

concentration discrepancies 

has not yet been resolved, 

selection of low Fe,Mg 

smectite is appropriate in the 

context of the present 

geochemical model. 

12 Calcium in solution 

is controlled by 

saturation with 

respect to calcite. 

Not all groundwaters are 

saturated with respect to calcite. 

Calcite will buffer pH 

changes due to changing 

P(CO2) more strongly than 

would be the case if it were 

absent. 

The evaluation of NWIS 

groundwaters indicated that 

most are saturated with 

respect to calcite. 

13 Concentrations of 

major species in 

groundwater. 

Concentrations were fixed and 

chosen to be representative of 

the modes of major species 

analyses in NWIS 

groundwaters. Variations could 

impact predictions of hazardous 

constituent concentrations 

The sulfate concentration will 

affect predictions of Ba 

concentration in groundwaters 

by ≈ ±0.3 log unit, as it is 

assumed that barite is the host 

mineral. The sulfate 

concentration will also affect 

the redox potential through 

equilibrium with the 

pyrite/goethite couple by a 

similar uncertainty. Other 

species will have negligible 

effect, as none participate 

directly in mass action 

reactions defining hazardous 

constituent concentrations. 

Selection of representative 

values for the major species is 

appropriate to ensure model 

verisimilitude with field 

observations. 
 

148 of 352

Table 3.16. Uncertainties in geochemical model parameters 

No. Parameter Estimated Uncertainty Impact on Findings 

1 Mineral solubility 

products of 

hazardous 

constituent 

mineral hosts. 

The primary uncertainty is associated with 

thermochemical (calorimetric) determinations of 

ΔHf

o , which display standard deviations of ≈

±0.5 - 1.0 kcal/mol in typical cases. Errors 

associated with the ΔGf

o of aqueous basis species 

are generally small, i.e., ≈ ±0.2 kcal/mol, 

although the uncertainty associated with some 

basis species could be larger, e.g., for Fe+2, Fe+3, 

HSeIn general, uncertainties associated with mineral 

solubility products could lead to over- or underpredicting the concentration of a hazardous 

constituent by ≈ ±1 order of magnitude. 

2 Dissociation 

constants of 

sulfide and 

selenide 

complexes of 

hazardous 

constituents. 

Experimentally determined uncertainties of 

dissociation constants of sulfide complexes are 

usually less than ±0.5 log unit. However, with 

the exception of Hg selenide complexes, all other 

selenide complex dissociation complexes have 

been estimated with potential uncertainties that 

could be as high as ±2 log units. 

Uncertainties associated with the correct 

identification and stoichiometry of sulfide and 

selenide complexes can lead to significant errors 

in estimated concentrations of some hazardous 

constituents, especially with respect to Cd, Hg, 

Pb, Se and Zn. Uncertainties are most acute 

where selenide complexes dominate over 

corresponding sulfide complexes, as is the case 

with the solubility of cinnabar (Hg,) and 

tiemannite (Se). The solubilities of CdS 

(greenockite), galena (Pb) and sphalerite (Zn) 

are affected only incrementally by selenide 

complexation. Therefore the uncertainties in 

predicted concentrations of Hg and Se are likely 

to be much larger, i.e., ±2 log units, than for Cd, 

Pb and Zn, i.e., ±0.5 log units. 

3 Arsenopyrite and 

gudmundite solid 

solution proxies. 

Field evidence suggests that the abundances of 

As and Sb in potable water aquifers are normally 

insufficient to cause saturation with respect to 

either arsenopyrite or gudmundite, although, 

with respect to Sb, it is probable that kermesite 

saturates under more acid conditions induced by 

higher P(CO2). Therefore, it was assumed that 

both As and Sb exist in solid solution in pyrite as 

arsenopyrite and gudmundite components, 

respectively, and their solid solution saturations 

were calibrated against the estimated modes of 

the concentrations of these elements in potable 

NWIS groundwaters. Uncertainties are 

approximately ±1 log unit for arsenopyrite SS, 

and ±2 log unit for gudmundite SS. 

The uncertainties in arsenopyrite SS and 

gudmundite SS likewise affect calculated 

concentrations of As and Sb in solution, except 

where the latter saturates with respect to 

kermesite. 

4 Activity 

coefficient 

estimates. 

The a parameter in the extended form of the 

Debye Huckel expression is estimated for almost 

all sulfide and all selenide complexes of 

hazardous constituents. 

Because potable waters have low total dissolved 

solids concentrations, uncertainties associated 

with the a parameter have minor and 

inconsequential effect on calculated 

concentrations of hazardous constituents in 

solution. 
 

149 of 352

No. Parameter Estimated Uncertainty Impact on Findings 

5 Rock-forming 

mineral solubility 

products. 

Uncertainties range from very low in the case of 

silica polymorphs (±0.1 log unit) to significant, 

as is the case with several clay minerals (≥ ±1.5 

log unit) depending on mineral stoichiometry. 

The rock forming minerals equilibrate with the 

groundwater and buffer pH and Eh. They 

therefore define the environmental conditions, 

which in turn affect the solubilities of the 

hazardous constituent mineral hosts. The pH 

and Eh of most groundwaters are buffered 

within a fairly narrow range. The impact of 

uncertainties in the solubility products of rockforming minerals is not explicitly evaluated in 

this study, but general consistency with field 

observations suggests that these uncertainties 

have a small effect on predicted hazardous 

constituent concentrations in most cases. 
 

150 of 352

Table 3.17. Uncertainties in chemical analyses of potable groundwaters 

No. Parameter Estimated 

Uncertainty 

Comments Impact on Findings 

Basic Chemistry 

1 pH ±0.1 pH unit Errors introduced through 

improper custodial handling could 

increase the uncertainty to ±0.5 

pH units or greater 

Associated uncertainty could result in errors in 

the calculated SI of hazardous constituent host 

minerals and hazardous constituent 

concentrations of as large as ±1.0 unit, although 

for most analyses, the error is likely to be much 

smaller, and the solubilities of several 

constituents are insensitive to pH. 

2 Major cations 

(Na, K, Mg, 

Ca) 

±5 % These analyses are usually quite 

reliable, especially those made 

during the last 15 years. 

Impact on the calculated SI of hazardous 

constituent host minerals and hazardous 

constituent concentrations is expected to be 

small, i.e., < ±0.1 unit. 

3 Major anions 

(Cl, SO4) 

±5 % These analyses are usually quite 

reliable, especially those made 

during the last 15 years. 

Impact on the calculated SI of hazardous 

constituent host minerals and hazardous 

constituent concentrations is expected to be 

small, i.e., < ±0.1 unit. 

4 Total HCO3

-

 ±10 % Errors introduced through 

improper custodial handling could 

increase the uncertainty to ±30% 

or greater. 

The calculated extent of carbonate complexing 

by Ba, Cd, Pb, U and Zn in groundwaters would 

be affected, but because carbonate complexes 

are not predominant, except for Pb at >0.1 bar 

P(CO2), the impact on calculated SI of host 

minerals is expected to be minor, i.e., << ±0.1 

unit. 

5 Minor cations 

(e.g., Al, Fe+2, 

Fe+3) 

±10 % When at the limits of detection, 

uncertainties could exceed ±50%. 

Errors introduced through 

improper custodial handling could 

increase the uncertainty to the 

point where the analytical result is 

essentially meaningless. 

Uncertainties associated with measurements of 

Fe+2 in potable waters could lead to very large 

errors (>> 5 units) in the calculated SI of 

hazardous constituent host minerals when used 

to calculate Eh.Uncertainties associated with 

measurement of Al in the calculated SI of 

hazardous constituent host minerals are likely to 

be small, i.e., < ±0.1 unit. 

6 Minor Anions 

(e.g., F, PO4) 

±10 % When at the limits of detection, 

uncertainties could exceed ±50%. 

Errors introduced through 

improper custodial handling could 

increase the uncertainty to the 

point where the analysis is 

essentially meaningless. 

Impact on calculated hazardous constituent 

concentrations is expected to be minor, as no 

analyses of minor anions were used in the 

calculation of either SIs of hazardous 

constituent host minerals or hazardous 

constituent concentrations. 

7 SiO2(aq) ±5 % Analyses are usually fairly 

reliable. 

Uncertainties in the calculated SI of hazardous 

constituent host minerals and hazardous 

constituent concentrations, associated with 

measurement of SiO2(aq), are likely to be small, 

i.e., < ±0.1 unit. 

8 Charge 

imbalance 

±30 % The calculated charge imbalance 

of many potable NWIS 

groundwaters is larger than 

generally considered acceptable. 

The root causes of such large 

imbalances are difficult to 

determine. 

Charge imbalance was ignored in the calculation 

of SIs of hazardous constituent host minerals. 

However, the large statistical dispersion in 

values of charge imbalance suggests cumulative 

uncertainties in the chemical analyses of 

individual constituents, especially pH and 

HCO3

-

, or to the omission of analyses of one or 

more major species. 
 

151 of 352

No. Parameter Estimated 

Uncertainty 

Comments Impact on Findings 

Hazardous Constituents 

9 As ±10 % For the most part, only the most 

sensitive analytical method, i.e. 

PLM40 with a detection limit of ≈

0.0002 mg/L, yields 87% 

quantitative measurements. 

The potential error in SI, due to analytical 

uncertainty, is ≈ 0.1 unit for the most sensitive 

analytical method. 

9 Ba ±5 % Modern analytical methods, e.g., 

PLM43 and PLM47 with a 

detection limit of 0.0002 mg/L, 

yield >95% quantitative 

measurements. 

The potential error in SI, due to analytical 

uncertainty, is <0.1 units for the most sensitive 

analytical methods. 

10 Cd ±50 % Only the most sensitive analytical 

methods, i.e. PLM43 and PLM47 

with a detection limit of ≈ 0.00004 

mg/L, yield meaningful 

measurements, of which only 24 

and 17% respectively are 

quantitative. 

The potential error in SI, due to analytical 

uncertainty, is ≈ 0.2 unit for the most sensitive 

analytical method. 

11 Hg ±70 % Most analytical methods have 

detection limits of ≈ 0.0001 mg/L, 

but only 0 - 14% yield 

quantitative measurements. 

The potential error in SI, due to analytical 

uncertainty, is ≈ 0.1 unit for the most sensitive 

analytical method. 

12 Pb ±20 % Only the most sensitive analytical 

methods, i.e. PLM43 and PLM48 

with a detection limit of ≈ 0.0001 

mg/L and ≈0.00006 mg/L, yield 

meaningful measurements, with 

49% and 82% giving quantitative 

data. 

The potential error in SI, due to analytical 

uncertainty, is ≈ 0.1 unit for the most sensitive 

analytical methods. 

13 Sb ±50 % Only the most sensitive analytical 

method, i.e. PLM43 with a 

detection limit of ≈0.00005 mg/L, 

yields meaningful measurements, 

of which only 18% are 

quantitative. 

The potential error in SI, due to analytical 

uncertainty, is ≈ 0.2 unit for the most sensitive 

analytical method. 

14 Se ±30 % Only the most sensitive analytical 

methods, i.e. PLM10 and PLM40 

with detection limits of 0.0008 

and 0.0003 mg/L respectively, 

yield meaningful measurements, 

of which 75 and 54% are 

quantitative. 

The potential error in SI, due to analytical 

uncertainty, is > 0.1 unit for the most sensitive 

analytical method. 

14 U ±20 % The most sensitive analytical 

methods, i.e., PHOS3 with a 

detection limit of ≈0.0004 mg/L 

and FL007 with a detection limit 

of ≈ 0.00001 mg/L respectively, 

yield 67% and 97% quantitative 

measurements. 

The potential error in SI, due to analytical 

uncertainty, is ≈ 0.1 unit for the most sensitive 

analytical methods. 

16 Zn ±5 % Modern analytical methods, e.g., 

PLM10 and PLM43, both with a 

detection limit of ≈ 0.0006 mg/L 

respectively, yield 82% and 94% 

quantitative measurements. 

The potential error in SI, due to analytical 

uncertainty, is <0.1 units for the most sensitive 

analytical methods. 
 

152 of 352

Table 3.18. Uncertainties relating to environmental variability in sampled NWIS groundwater compositions 

No. Parameter Estimated Variability Impact on Findings 

1 Temperature and 

pressure 

The temperatures of most groundwaters within 

the 48 contiguous states probably fall between 5 

oC and 30oC (see Figure 3.52). Hydrostatic 

pressures in potable water aquifers likely range 

from 1 to 10 bar. 

Variations in log K for the dissolution reaction 

of a host mineral between 5 and 25o range from 

≈ –1.0 to ≈ +2.5 for the host minerals under 

consideration. Pressure changes of up to 10 

bars would have negligible impact on 

thermodynamic equilibria. 

2 Oxidation state Oxidation state could range from highly 

reducing, i.e., close to the decomposition of 

water with respect to the evolution of H2, to 

oxidizing, i.e., in equilibrium with air. 

Such large variations in oxidation state would 

lead to changes in the identities of mineral hosts 

controlling hazardous constituent saturations. 

Variations in SI of specified mineral hosts are 

likely to range over ± 2 – 3 log units. A general 

consistency of SI distributions in NWIS potable 

groundwaters suggests that most fall into a 

narrower range of oxidation states. 

3 Natural variations 

in hazardous 

constituent 

concentrations 

Examination of hazardous constituent 

distributions of concentration in soils and 

sediments in the National Geochemistry 

Database shows that the standard deviation of 

an approximated log normal distribution 

averages about 0.3. Therefore at 3 standard 

distributions (accounting for 99.7% of all), the 

range is approximately ±1 log unit. If the same 

constituent distributions apply to potable water 

aquifer host rocks, then whether or not a 

mineral host controls the aqueous concentration 

of a hazardous constituent would depend on 

whether the concentration distribution in the 

aquifer host rock permits saturation. 

Estimating the natural variability of hazardous 

constituent concentrations in potable waters 

from the corresponding distributions in aquifer 

host rocks is difficult to establish without 

detailed analysis, as many geochemical factors 

are involved. The measured distributions, 

based on chemical analyses are confounded 

with analytical uncertainties, and are therefore 

broader than would be found in nature. If the 

concentration distributions of Ba, U and Zn in 

NWIS potable waters are examined, it appears 

that natural variability does not exceed ± 1.2, 

±2.0 and ± 1.5 log units, respectively. It is 

expected that the other hazardous constituent 

concentrations would fall into similar ranges, 

but they cannot be accurately estimated because 

the mean of the distribution is at or below the 

detection limit. 

4 Host rock 

mineralogy 

Aquifer host rocks range in mineralogy from 

essentially pure arenites through impure arkoses 

to dolomitic limestones. Some shallow 

unconfined aquifers may be hosted in 

unconsolidated sediments. Under reducing 

conditions, the stability of these identified 

mineral hosts is relatively unaffected by these 

differing mineralogical environments. The pH 

of groundwaters in partial equilibrium with the 

mineral assemblages may vary by as much as 

±2 pH units, which, under oxidizing conditions 

could destabilize the mineral hosts found under 

these conditions, e.g., cerussite or otavite, and 

perhaps hemimorphite for Pb, Cd and Zn, 

respectively. 

Most of the hazardous constituent 

concentrations in NWIS potable groundwaters 

strongly suggest equilibrium with mineral hosts 

under reducing conditions in the presence of a 

pyrite/goethite or similar buffer. Observed 

concentration distributions of several hazardous 

constituents also suggest a positive skew, which 

may be due to equilibration under oxidizing 

conditions with respect to higher-solubility 

oxidized mineral hosts at varying pH values, or 

the skew may be due to analytical artifacts. We 

conclude that under reducing conditions, 

mineralogical variations could cause small 

changes, i.e., ≤ 1 log unit in concentration. A 

very minor population of NWIS groundwaters 

has been determined to reside under mildly 

reducing to oxidizing conditions. This lead to 

an increase in equilibrium concentration over 

reducing conditions by +2 to 3 log units, or 

greater. 
 

153 of 352

No. Parameter Estimated Variability Impact on Findings 

5 Extent to which 

system is at 

equilibrium 

Few studies have been conducted to rigorously 

analyze the extent to which sedimentary 

systems have attained equilibrium with pore 

waters. Generally, when equilibrium between 

detrital silicates and the groundwater is not 

attained, diagenetic processes reflect a 

progressive approach to equilibrium, which 

may involve the production of metastable 

secondary minerals such as opal-CT and 

smectites. As noted above (Table 3.3, Item 5) 

the lack of thermodynamic equilibrium between 

rock-forming minerals and groundwaters 

generally does not materially affect the aqueous 

concentrations of hazardous constituents. With 

respect to hazardous constituent mineral hosts, 

it is expected that most will closely approach 

equilibrium in reducing groundwater provided 

that the system is not perturbed through the 

ingress of oxygen. One issue of concern, 

however, relates to attainment of equilibrium 

between SO4

-2 and H2S(aq) . Redox 

equilibrium in this reaction may not be 

achieved, and this should be borne in mind 

when conducting model analyses. Non 

attainment of equilibrium between these species 

also impacts perturbations in equilibrium 

between sulfide mineral hosts and the 

groundwater due to changing pH, where 

increased solubility can lead to only partial 

oxidation of S(-II) to native sulfur rather than 

S(VI). 

Non-attainment of equilibrium between 

aqueous sulfur species could lead to significant 

dispersion in the calculated SIs of sulfide 

mineral hosts. However, the statistical 

dispersion of the calculated SIs for sphalerite 

(ZnS) is not significantly different from that of 

barite (BaSO4) or uraninite (UO2). Because the 

SI was calculated using the measured SO4

-2

concentration, and calculation of H2S(aq) was 

based on assumed equilibrium between SO4

-2

and H2S(aq), this observation lends credence to 

the belief that non-attainment of equilibrium 

between aqueous sulfur species is not a 

significant issue in the sampled NWIS potable 

groundwaters. 

6 Anthropogenic 

contaminants 

Anthropogenic contamination by certain 

hazardous constituents, e.g., As, Hg, Pb, Se, in 

surficial soils and sediments could be the source 

of subsequent groundwater contamination, due 

to solubilization and infiltration. Clear 

indications of such processes are usually 

localized, and inadvertent exploitation of 

potable waters that have been contaminated in 

that manner are rare. 

It is not expected that anthropogenic 

contamination of groundwaters materially 

affects the compositions of the NWIS 

groundwaters selected for this study. 
 

154 of 352

Table 3.19. Estimated Uncertainties contributing to the Aggregate Uncertainty in Log K of the Solubility Product 

of the Hazardous Constituent Mineral Host1

Item As Ba Cd Hg Pb Sb Se U Zn 

Host mineral Arsenopyrite SS 

Barite CdS Cinnabar 

Galena Gudmundite SS 

Tiemannite 

Uraninite Sphalerite 

Relevant Uncertainty Contributions from Table 3.15 

Item 6 0.85 0.26 0.7 1.7 0.7 0.85 1.8 0.6 0.3 

Item 7 0 0 0 0.5 0 0 0.5 0 0 

Item 11 0.3 0.3 0.3 0.3 0.3 0.3 0 0. 0.3 

Relevant Uncertainty Contributions from Table 3.16 

Item 1 0.5 0.3 0.23 0.48 0.35 0.5 0.5 0.3 0.26 

Item 2 0 0 0.5 1.0 0.5 0 1.0 0 0 

Item 3 1.0 0 0 0 0 1.0 0 0 0 

Relevant Uncertainty Contributions from Table 3.17 

Item 1 0.1 0 0 0 0.1 0.1 0.1 0 0 

Item Haz. Const. 0.1 0.1 0.2 0.1 0.1 0.2 0.1 0.1 0.1 

Aggregate Uncertainty 

σ(+) 1.17 0.44 0.66 1.26 0.69 1.18 1.23 0.68 0.65 

σ(-)

2 1.49 0.51 0.96 2.11 0.99 1.50 2.18 0.32 0.71 

Log K + σ(+) 56.83 -9.53 -13.43 -36.24 -13.71 34.97 -46.28 3.15 -9.35 

Log K3 55.66 -9.97 -14.09 -37.50 -14.40 33.76 -44.10 3.83 -10.00 

Logk - σ(-) 54.17 -10.48 -14.09 -39.61 -15.39 32.26 -42.87 4.15 -10.71 

1

 Values refer to one σ, i.e., one standard deviation from the mean. 2

 The larger negative uncertainty takes into account the bias introduced by assuming a model temperature that is about 10-15oC 

higher than average ambient aquifer temperatures. 3

 Best-estimate value used in equilibrium calculation. See Table 3.14. 

Because the geochemical model is calibrated against field measurements, as discussed in Section 

3.5.2, the major uncertainty associated with identification of the specific redox buffers defining 

oxidation state is implicitly taken into account. Uncertainties in log K of the solubility products 

of the mineral hosts due to differences between the modeled and observed temperatures are also 

partially compensated by the calibration step. A biased uncertainty arising from model 

calculations made at 25oC is, however, included to account for the modal ambient temperature of 

~12oC observed in potable water aquifers, as shown in Figure 3.52. This figure gives the 

frequency histogram for the temperatures of the NWIS groundwaters evaluated in this study. For 

convenience, uncertainties associated with sulfide and selenide complexation are folded into the 

uncertainty associated with the log K values of the solubility products. Although this approach is 

not strictly correct, it is adopted in order to facilitate calculation of the overall model uncertainty. 

The aggregate uncertainty in Table 3.19 is estimated by taking the square root of the sum of the 
 

155 of 352

squares of the uncertainties associated with each parameter, an approach that is commonly 

adopted as a convenient technique for estimating this value. Note that the upper and lower 

uncertainties differ to reflect the bias introduced by assuming a 25oC temperature. 

Fig. 3.52. Histogram showing the temperature (oC) distribution of more than 35,000 groundwater analyses 

selected from the NWIS Database. 

In order to generate an uncertainty envelope for the equilibrium calculations, the computed upper 

and lower values of Log K for the solubility products of the respective mineral hosts for each 

element were used to calculate the concentrations of the respective hazardous constituents at 

discrete intervals of log P(CO2). The calculations were performed by varying the log K value 

separately for each hazardous constituent, while fixing all other log K values for the other 

hazardous constituents at the calibrated value. The results are plotted in Figure 3.51 in the form 

of a grey envelope, which delineates the ±σ limits of uncertainty about the calibrated value. Note 

that the total concentrations of Cd, Hg, Pb and Zn are affected in varying degrees by 

complexation with selenide, as would result from the ±σ variation in log K for tiemannite 

(HgSe). These variations are not taken into account in the present analysis, but further study of 

this phenomenon is merited in future more rigorous studies. The assumption that the Se 

concentration in solution is determined by tiemannite saturation has the unintended consequence 
 

156 of 352

of swamping out variations in Hg aqueous concentration imposed by the -σ variation in log K for 

cinnabar, because Hg selenide complexation accounts for a substantial fraction of the total Hg 

concentration in solution. Hence an artifact is created in the form of a highly asymmetric grey 

uncertainty envelope about the calibrated value displayed by Hg in Figure 3.51. 

3.5.4.2 Discussion Relating to Uncertainties 

Inspection of the graphs presented in Figure 3.51 in most cases shows excellent agreement 

between the calculated equilibrium concentration of a given constituent at log P(CO2) = -2 and 

the measured or estimated mean concentration in NWIS groundwaters at the same P(CO2). In 

other words, the predicted concentration from the geochemical model agrees reasonably well 

with the observed data from groundwater analyses. This agreement is expected as a consequence 

of the calibration process, which is applied to all constituents, except Ba and Cd, as described in 

Section 3.5.2. Furthermore, the calculated aggregate uncertainties at ±1σ, illustrated with the 

grey shading, are on average about half the ±2σ range estimated independently for the variability 

in hazardous constituent concentrations in NWIS potable groundwaters. Thus, despite the 

obvious crudity in estimating aggregate uncertainty, it is comparable with the statistical 

dispersion actually observed in potable groundwater. 

Significant discrepancies arise, however, with respect to the calculated concentrations of Cd, Hg, 

and Sb when compared with the corresponding field estimates of the mean concentrations in 

NWIS potable groundwaters. This requires further comment. In all three, the initial calibration 

was based on the selection of the mode of the SI for the minerals CdS (Figure 3.36a), cinnabar 

(Figure 3.37a) and gudmundite solid solution (Figure 3.39a), bearing in mind the fact that the 

analyses most trustworthy are those made with the most sensitive analytical methods currently 

available. The evaluation did not take into account the most likely statistical distribution based 

on the percentage of quantitative measurements versus non-detects in NWIS waters using the 

most sensitive analytical method for a given constituent, as is evaluated in this section. The fact 

that only 24(17)% have detects for Cd, only 12% for Hg, and only 18% for Sb (see Table 3.9, for 

all samples analyzed with the most sensitive analytical methods) suggests that many low- 
 

157 of 352

concentration samples for these constituents are not accounted for. In other words, the real mean 

values of the log concentrations for Cd, Hg and Sb falls below the detection limits of the most 

sensitive analytical methods currently in routine use. An attempt was made in Figure 3.51 to 

show an adjusted real concentration range in NWIS potable groundwaters, assuming the the 

percentage of detectable values represents an observed upper “tail” of lognormally distributed 

values. However, the calibration of log K values for the geochemical model was conducted 

without taking this bias into account; thus the presently predicted concentrations of these 

constituents as a function of P(CO2) are overstated, and are therefore inherently conservative. A 

reappraisal of the thermodynamic controls affecting the concentrations of Cd, Hg and Sb in 

potable groundwaters is clearly necessary. Such corrections would lead to the conclusion that 

these three constituents would be even less likely to adversely impact water quality upon CO2

intrusion than the present equilibrium concentrations indicate. 

If the revised estimated mean concentration of Cd in NWIS goundwaters was taken into account, 

then the modal value of SI for CdS would be ≈ -1.5, rather than -0.5, indicating that CdS is even 

less likely to be present as a discrete phase, and is most probably present in solid solution in 

sphalerite or possibly galena, although saturation with respect to cadmoselite still remains a 

plausible option in some groundwaters. It should be noted that the SI for CdS is unlikely to be 

depressed significantly be selenide complexing of Cd+2, as such species appear to be relatively 

minor contributors (see Table 3.21 under CdS (greenockite)). Figure 3.51 shows that the 

predicted versus estimated mean concentration of Hg in solution at log P(CO2) = -2 differs by 

about 2 log units. Such a large correction would depress the modal SI for cinnabar and 

quicksilver to ≈ -2 in both cases. Saturation with respect to tiemannite is likely to be similarly 

affected. The extent of undersaturation could be offset by assuming that the log K value of the 

solubility product is -38.08 (Appendix D, Table D1), based on calorimetric data, rather than the 

calibrated value of -37.5. A correction of this magnitude would still leave cinnabar 

undersaturated, and is consistent with several field observations suggesting that Hg normally 

partitions preferentially in pyrite (see Section 3.2). With respect to Sb, Figure 3.51 suggests a 

correction of approximately -1.5 log units to the calibrated value of gudmundite in solid solution. 

Such a correction would defer saturation with respect to kermesite until log P(CO2) exceeds -0.5. 
 

158 of 352

3.5.5 System Variations with P(CO2) and the Impact of Complexation 

The underlying reasons for the response of hazardous constituent concentrations in potable 

groundwaters to increasing P(CO2) can be better understood through an examination of the 

induced variations in pH, Eh and P(H2S), and the progressive redistribution of participating 

aqueous complexes in solution in response to these changes. This examination is conducted in 

the following sections. 

3.5.5.1 Variation of pH, Eh and P(H2S) with P(CO2) 

Species distributions were made at 25°C over a range of CO2 partial pressures from log P(CO2) = 

-4 to +1, reflecting the full range of partial pressures either in natural shallow groundwaters, or in 

those intruded by CO2 escaping from an underlying deeply buried aquifer. The variation in the 

equilibrium partial pressure of CO2 leads to variations in the concentration of CO2(aq), which, 

being a weak acid, hydrolyses and lowers pH, as shown in Table 3.20. The pyrite/goethite phase 

boundary also defines the Eh at any specified pH, where Eh is inversely related to pH. 

Table 3.20. Variation of key dependent compositional parameters in groundwaters with variation in CO2 partial 

pressure 

Log P(CO2) (bar) pH Eh (V) LogP(H2S) (bar) 

-4 8.906 -0.314 -8.713 

-3 8.136 -0.257 -8.815 

-2 7.378 -0.200 -8.912 

-1 6.684 -0.149 -8.999 

0 6.051 -0.103 -9.075 

+1 5.574 -0.069 -9.127 

The calculated values of log P(H2S) are also listed in Table 3.15 as a function of Log P(CO2). As 

noted under Section 3.4.5.1, P(H2S) is a measure of the hydrogen sulfide activity in solution, and 

has an important bearing on the extent of sulfide complexing of heavy metals in solution, 

including Cd, Hg, Pb and Zn. The calculated log P(H2S) at log P(CO2) = -2 , the modal value 
 

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calculated for NWIS groundwaters illustrated in Figure 3.17, is –8.91, which corresponds closely 

to the calculated NWIS groundwater modal value for this species, -8.95, in Figure 3.18. It is 

interesting to note that the variation in P(CO2) over five orders of magnitude, from log P(CO2) = 

-4 to +1, results in only a minor variation in log P(H2S), i.e., from –8.7 to –9.1 for the specified 

base case conditions given in Table 3.20. 

3.5.5.2 Nature of Aqueous Complexation and its Impact on Mineral Solubility 

The shift in pH and Eh contributes to the changing solubilities of the trace-element host minerals 

and their stability in relation to other potential mineral hosts. It is, therefore, instructive to 

investigate the underlying reasons why the concentrations of some elements are sensitive to 

changes in the partial pressure of CO2, whereas others are not, and why. Table 3.21 lists how the 

principal aqueous species in equilibrium with the host minerals vary with P(CO2) for each of the 

elements studied. All species are listed that constitute greater than 0.001% of the total speciation 

at any value of log P(CO2) between -4 and +1. 

For most minerals, the changing solubility as a function of P(CO2) can be ascribed primarily to a 

change in pH, and not to increased complexation by carbonate species. For Hg, As, Sb, and Se, 

neutral and anionic species are predominant, and carbonate species play no part. Enhanced 

solubility due to carbonate complexation occurs with Ba, Cd, and Pb. However, only with Pb 

does carbonate complexation, as PbCO3(aq), dominate the aqueous speciation. The molal 

percentage of PbCO3(aq) increases only incrementally over a 5-order of magnitude change in 

both total Pb in solution and P(CO2). With Ba and Cd, the predominant carbonate species is the 

bicarbonate, MHCO3

-

 (M = Ba or Cd), but it never becomes the major species contributing to 

aqueous solubility with either element. Uranium solubility is dominated by the neutral U(IV) 

species UO2(aq). Although the U(VI) species UO2

+2 complexes strongly with carbonate, this 

species is present in inconsequential concentrations under reducing conditions, and therefore the 

carbonate complexes, UO2(CO3)3

-4, UO2(CO3)2-2 and UO2CO3(aq), are present only in minor 

amounts, even at the highest P(CO2) studied. 

. 
Table 3.21. Concentration of aqueous species of hazardous constituents in aqueous solution in equilibrium with specified host min

CO2 partial pressure. 

Arsenopyrite Solid Solution Component 

Log P(CO2) (bar) -4 -3 -2 -1 0 Concentration 

 Species 

mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % 

H3AsO3(aq) 1.2617E-09 63.60 1.0346E-08 91.12 7.8402E-08 98.30 4.7269E-07 99.64 2.2437E-06 99.91 H2AsO3- 7.1869E-10 36.23 1.0059E-09 8.86 1.3530E-09 1.70 1.6980E-09 3.579E-01 1.9655E-09 8.752E-02 AsS(OH)(SH)- 3.0122E-12 1.518E-01 2.6413E-12 2.326E-02 2.2651E-12 2.840E-03 <0.001 <0.001 HAsO4-2 3.6050E-13 1.817E-02 2.2218E-13 1.957E-03 <0.001 <0.001 <0.001 MgAsO4- 5.2383E-14 2.640E-03 <0.001 <0.001 <0.001 <0.001 CaAsO4- 4.5689E-14 2.303E-03 <0.001 <0.001 <0.001 <0.001 MgHAsO4(aq) 1.2901E-14 6.503E-04 <0.001 <0.001 <0.001 <0.001 CaHAsO4(aq) 1.0005E-14 5.043E-04 <0.001 <0.001 <0.001 <0.001 FeAsO4(aq) <0.001 <0.001 <0.001 4.1048E-12 8.653E-04 3.9021E-11 1.738E-03 

Barite 

Log P(CO2) (bar) -4 -3 -2 -1 0 +1 Concentration 

Species 

mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/k

Ba+2 9.0983E-07 97.35 9.7134E-07 97.78 1.1677E-06 96.97 1.6138E-06 94.71 2.6912E-06 89.92 6.493BaCO3(aq) 1.4885E-08 1.59 4.4642E-09 4.494E-01 1.5267E-09 1.268E-01 7.6410E-10 4.484E-02 5.6868E-10 1.900E-02 1.048BaHCO3+ 9.8517E-09 1.05 1.7525E-08 1.76 3.4959E-08 2.90 8.9372E-08 5.24 3.0107E-07 10.06 1.849BaCl+ 4.6233E-11 4.947E-03 4.8169E-11 4.849E-03 5.4081E-11 4.491E-03 6.6273E-11 3.889E-03 9.1111E-11 3.044E-03 1.538BaOH+ 1.9472E-11 2.083E-03 <0.001 <0.001 <0.001 <0.001 
Table 3.21. Continued.CdS (Greenockite) 

Log P(CO2) (bar) 

-4 -3 -2 -1 0 

Concentration 

Species 

mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % 

Cd(OH)2(HS)- 2.3468E-08 97.03 4.0056E-09 87.40 7.1093E-10 56.15 1.4793E-10 19.51 3.6015E-11 1.48 CdS(aq) 5.1286E-10 2.12 5.1286E-10 11.19 5.1286E-10 40.51 5.1286E-10 67.63 5.1286E-10 21.01 Cd(OH)2(HSe)- 1.6923E-10 6.997E-01 2.8885E-11 6.303E-01 5.1265E-12 4.049E-01 1.0667E-12 1.407E-01 2.5971E-13 1.064E-02 CdSe(aq) 3.5481E-11 1.467E-01 3.5481E-11 7.742E-01 3.5481E-11 2.80 3.5481E-11 4.68 3.5481E-11 1.45 Cd+2 <0.001 3.4753E-14 7.583E-04 1.5300E-12 1.208E-01 5.1586E-11 6.80 1.3747E-09 56.32 CdHCO3+ <0.001 <0.001 1.3916E-13 1.099E-02 8.6793E-12 1.14 4.6725E-10 19.14 CdCl+ <0.001 <0.001 2.0816E-14 1.644E-03 6.2233E-13 8.207E-02 1.3672E-11 5.601E-01 CdCO3(aq) <0.001 <0.001 <0.001 5.5262E-14 7.288E-03 6.5727E-13 2.693E-02 CdOH+ <0.001 <0.001 <0.001 1.4958E-14 1.973E-03 8.0528E-14 3.299E-03 CdSO4(aq) <0.001 <0.001 <0.001 <0.001 5.4951E-14 2.251E-03 

Cinnabar 

Log P(CO2) (bar) -4 -3 -2 -1 0 +Concentration 

Species 

mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % m

HgSe(aq) 2.1380E-09 68.12 2.1380E-09 68.13 2.1380E-09 68.13 2.1380E-09 68.13 2.1380E-09 68.13 2HgS(aq) 1.0000E-09 31.86 1.0000E-09 31.87 1.0000E-09 31.87 1.0000E-09 31.87 1.0000E-09 31.87 1HgS2-2 4.2968E-13 1.369E-02 <0.001 <0.001 <0.001 <0.001 HgS(HS)- 2.5805E-13 8.222E-03 3.4863E-14 1.111E-03 <0.001 <0.001 <0.001 
Table 3.21. Continued.Galena 

Log P(CO2) (bar) 

-4 -3 -2 -1 0 

Concentration 

Species 

mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/k

PbCO3(aq) 2.6250E-13 1.918E-02 3.3164E-12 2.417E-01 4.1534E-11 2.94 5.0714E-10 26.59 6.0318E-09 74.20 6.763Pb+2 1.7060E-14 1.243E-03 7.5210E-13 5.327E-02 2.5440E-11 1.33 6.8289E-10 8.40 1.022PbS(aq) 1.2589E-09 91.97 1.2589E-09 91.75 1.2589E-09 89.16 1.2589E-09 65.99 1.2589E-09 15.49 1.258Pb(CO3)2-2 4.5836E-14 3.348E-03 1.7084E-13 1.245E-02 6.9837E-13 4.946E-02 3.9345E-12 2.063E-01 3.0843E-11 3.794E-01 5.445PbSe(aq) 1.0965E-10 8.01 1.0965E-10 7.99 1.0965E-10 7.77 1.0965E-10 5.75 1.0965E-10 1.35 1.096Pb(OH)+ 7.1397E-15 5.216E-04 5.3504E-14 3.899E-03 3.9085E-13 2.768E-02 2.4377E-12 1.278E-01 1.3123E-11 1.614E-01 4.899PbCl+ <0.001 <0.001 <0.001 8.8924E-14 4.662E-03 1.9536E-12 2.403E-02 2.005Gudmundite Solid Solution Component 

Log P(CO2) (bar) 

-4 -3 -2 -1 0 

Concentration 

Species 

mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mo

Sb(OH)3(aq) 1.2617E-11 99.72 1.0346E-10 99.95 1.1415E-09 99.99 1.3935E-09 100.00 1.6553E-09 100.00 1.8Sb(OH)4- 3.5686E-14 2.820E-01 4.9945E-14 4.825E-02 9.7811E-14 8.568E-03 2.4850E-14 1.783E-03 7.1924E-15 4.345E-04 2.8
Table 3.21. Continued.Tiemannite 

Log P(CO2) (bar) 

-4 -3 -2 -1 0 

Concentration 

Species 

mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % m

ZnSe(aq) 1.4454E-08 85.46 1.4454E-08 86.20 1.4454E-08 86.33 1.4454E-08 86.35 1.4454E-08 86.36 1HgSe(aq) 2.1380E-09 12.64 2.1380E-09 12.75 2.1380E-09 12.77 2.1380E-09 12.77 2.1380E-09 12.77 2PbSe(aq) 1.0965E-10 6.483E-01 1.0965E-10 6.539E-01 1.0965E-10 6.548E-01 1.0965E-10 6.550E-01 1.0965E-10 6.551E-01 1CdSe(aq) 3.5481E-11 2.098E-01 3.5481E-11 2.116E-01 3.5481E-11 2.119E-01 3.5481E-11 2.120E-01 3.5481E-11 2.120E-01 3FeSe(aq) 6.0651E-12 3.586E-02 3.0077E-12 1.794E-02 1.5312E-12 9.145E-03 8.4127E-13 5.026E-03 5.0044E-13 2.990E-03 3Cd(OH)2(HSe)- 1.6923E-10 1.00 2.8885E-11 1.722E-01 5.1265E-12 3.062E-02 1.0667E-12 6.373E-03 2.5971E-13 1.552E-03 9Uraninite 

Log P(CO2) (bar) 

-4 -3 -2 -1 0 

Concentration 

Species 

mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mo

UO2(aq) 4.0476E-09 99.92 4.0476E-09 99.99 4.0476E-09 100.00 4.0476E-09 100.00 4.0476E-09 99.99 4.04UO2(CO3)3-3 2.8238E-15 6.971E-05 6.5828E-15 1.626E-04 4.1055E-14 1.014E-03 4.1055E-14 1.014E-03 4.2100E-13 1.040E-02 5.54UO2(CO3)2-2 6.8029E-16 1.679E-05 5.1132E-15 1.263E-04 1.9863E-14 4.907E-04 1.9863E-14 4.907E-04 1.2075E-13 2.983E-03 1.5UO2CO3(aq) 6.4301E-19 1.587E-08 1.6382E-17 4.047E-07 4.0298E-16 9.956E-06 4.0298E-16 9.956E-06 8.9561E-15 2.212E-04 1.7UO2+ 2.0264E-17 5.002E-07 1.9186E-16 4.739E-06 1.7552E-15 4.336E-05 1.7552E-15 4.336E-05 1.3367E-14 3.302E-04 8.5HUO3- 3.3530E-12 8.277E-02 5.7230E-13 1.414E-02 1.0158E-13 2.509E-03 1.0158E-13 2.509E-03 2.1140E-14 5.222E-04 5.1
Table 3.21. Continued. Sphalerite 

Log P(CO2) (bar) 

-4 -3 -2 -1 0 

Concentration 

Species 

mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % mol/kgH2O mol % m

ZnS(aq) 3.1623E-07 95.59 3.1623E-07 95.28 3.1623E-07 88.66 3.1623E-07 28.97 3.1623E-07 1.44 3ZnSe(aq) 1.4454E-08 4.37 1.4454E-08 4.36 1.4454E-08 4.05 1.4454E-08 1.32 1.4454E-08 6.583E-02 1ZnOH+ 9.2743E-11 2.804E-02 6.9500E-10 2.094E-01 5.0770E-09 1.42 3.1665E-08 2.90 1.7047E-07 7.764E-01 6ZnS(HS)- 1.2125E-11 3.665E-03 1.6382E-12 4.936E-04 <0.001 <0.001 <0.001 Zn+2 9.4721E-12 2.863E-03 4.2567E-10 1.283E-01 1.8690E-08 5.24 6.2637E-07 57.38 1.6471E-05 75.02 2ZnO(aq) 5.6732E-12 1.715E-03 7.1675E-12 2.160E-03 8.9764E-12 2.517E-03 1.0960E-11 1.004E-03 <0.001 ZnCO3(aq) 2.7953E-12 8.450E-04 3.5315E-11 1.064E-02 4.4229E-10 1.240E-01 5.4004E-09 4.947E-01 6.4231E-08 2.925E-01 7ZnHCO3+ 2.6012E-13 7.863E-05 1.9493E-11 5.874E-03 1.4240E-09 3.992E-01 8.8814E-08 8.14 4.7813E-06 21.78 1ZnSO4(aq) <0.001 9.5241E-12 2.870E-03 3.4878E-10 9.779E-02 8.5090E-09 7.795E-01 1.3598E-07 6.193E-01 8ZnCl+ <0.001 <0.001 4.3321E-12 1.215E-03 1.2951E-10 1.186E-02 2.8454E-09 1.296E-02 2Zn(OH)Cl(aq) <0.001 2.4503E-13 7.383E-05 1.7271E-12 4.842E-04 1.0107E-11 9.259E-04 <0.001 
 

165 of 352

Arsenic and Sb behave similarly to each other, where aqueous species are dominated by neutral 

oxy-complex molecules of each element in the (III) state. Traces of As(V) oxy-anionic and 

neutral species are present, including H2AsO4

-

 and complexes of Mg+2, Ca+2 and Fe+3. The 

omission of corresponding species in the Sb system does not mean that they are absent, only that 

thermodynamic data are unavailable and no evidence has so far indicated they might be 

important. Both As and Sb complex with sulfide, but under the conditions of the current 

simulation, such complexation is negligible. Selenium complexation is also found to be 

negligible. Neither sulfide nor selenide complexes of either Ba or U are known. 

At low P(CO2), Cd speciation is dominated by Cd(OH)2(HS)- (relative concentration = 97 

mol%) at P(CO2) = 0.0001 bar, but declines progressively with increasing P(CO2) until it 

becomes inconsequential at P(CO2) = 10 bar. CdS(aq), a minor species at low P(CO2), increases 

to a maximum relative concentration of 68 mol% at P(CO2) =0.1 bar, but falls with further 

increase in P(CO2). CdSe(aq) and Cd(OH)2(HSe)-

 are present as minor species, CdSe(aq) 

following CdS(aq) and reaching a maximum relative concentration of about 5 mol% at P(CO2) = 

0.1 bar. With increasing P(CO2), Cd+2 increases progressively to a maximum relative 

concentration at P(CO2) = 1 bar, then begins to decline as CdHCO3+

, whose relative importance 

increases progressively with increasing P(CO2), and becomes predominant at P(CO2) = 10 bar, 

with a relative concentration of 53 mol%. The complex interaction between sulfide, selenide, 

bicarbonate and the uncomplexed Cd cation leads to complex CdS (greenockite) solubility 

behavior with respect to P(CO2), as is illustrated in Figure 3.51. 

Hg speciation is almost completely dominated by neutral oligomeric species represented by 

HgS(aq) and HgSe(aq), and the solubility of cinnabar is consequently essentially independent of 

P(CO2). The presence of selenide in solution effectively increases cinnabar solubility by 

approximately 50 mol%. 

The behavior of galena solubility as a function of P(CO2) is dominated primarily by the 

oligomeric species PbS(aq) at low P(CO2), but with increasing P(CO2), the carbonate complex, 

PbCO3(aq), becomes increasingly important until it predominates at P(CO2) > 1 bar. PbSe(aq) 

follows PbS(aq) in behavior, but increases galena solubility by only about 10 mol%. Pb+2 
 

166 of 352

increases progressively to a maximum relative concentration of 13 mol% at P(CO2) = 10 bar. By 

analogy with Cd+2, it is possible that the relative concentration of this species would decline at 

even greater P(CO2), as carbonate species become predominant. The increase in galena solubility 

with P(CO2) is due to the formation of the Pb(CO3)(aq) complex, and occurs when Log P(CO2) 

exceeds -1. 

In specifying the input parameters, we recognize that cadmoselite, tiemannite and clausthalite are 

all potential candidates for controlling Se(II) activity in solution, but it is not clear which is most 

plausible. Tiemannite was selected, because of the availability of thermochemical data for Hg 

selenide complexes (Mehra and Gubeli, 1971). A further constraint is imposed by Gibbs Phase 

Rule for equilibrium assemblages, which requires that only one selenide can be selected, unless 

saturation with respect to one or more sulfides of Cd, Hg, Pb, and Zn is suppressed. With the 

selection of tiemannite, and incorporation of selenide complexes, the saturation index for 

ferroselite is depressed to ~ -15 (Section 3.4.5.2). This suggests that Se substitution for S in 

pyrite is probably small. Although Ryser et al. (2005) have identified the association of selenian 

pyrite and dzharkenite, a ferroselite polymorph in shales associated with Western phosphate 

deposits in the United States, we believe that such an occurrence is unusual and is not 

representative of potable aquifer host rocks in general. Western phosphate shales are exceedingly 

rich in Se, averaging about 275 mg/kg rock. This contrasts with the average abundance of Se 

measured in surficial sediments and soils in the United States of no more than 1 mg/kg of 

material (Section 3.6). Western shales therefore represent a case of extreme selenium enrichment 

by a factor of perhaps as much as 1,000, where it must be presumed that all Cd, Hg, Pb, and 

perhaps some or all of the Zn are stabilized as selenides rather than sulfides, and there is 

sufficient Se in excess to precipitate FeSe2. 

Selenium speciation in solution saturated with respect to tiemannite is completely dominated by 

complexation with Zn and Hg as the oligomeric species ZnSe(aq) and HgSe(aq). Because 

sphalerite is relatively soluble in groundwaters, Zn complexing predominates, constituting 86 

mol% of Se species in solution, the balance being made up primarily by HgSe(aq). Under 

alkaline conditions where P(CO2) = 0.0001 bar, the Cd species Cd(OH)2(HSe)–

 begins to make 
 

167 of 352

an appearance, but only to the extent of about 1 mol% in relative concentration. Other selenide 

species, including CdSe(aq), PbSe(aq), and FeSe(aq), never reach significant concentrations in 

the current simulation. 

The solubility of sphalerite at low P(CO2) is determined primarily by the oligomeric species 

represented by ZnS(aq), which predominates in solution (~ 90+ mol%) until P(CO2) exceeds 

0.01 bar, thereafter falling to insignificant concentrations as P(CO2) reaches 10 bar. The Se 

analogue, ZnSe(aq), behaves similarly, but constitutes no more than 5 mol% of the total relative 

concentration. With increasing P(CO2), uncomplexed Zn+2 and ZnHCO3

-

 become predominant, 

representing 56 and 43 mol% respectively of the relative concentration at 10 bar. The behavior of 

Zn species with respect to sphalerite saturation is somewhat analogous to that of Cd species with 

respect to CdS (greenockite) saturation, but with the important distinction that a Zn analogue to 

the species Cd(OH)2(HS)–

 has not been identified. The variation of sphalerite solubility with 

P(CO2) actually shows greater affinity with that of galena, where both are insensitive to P(CO2) 

until it exceeds ~ 0.01 bar as carbonate complexes progressively overwhelm the sulfide 

complexes. 

Finally, it should be noted that other species, including complexes of Cl- and SO4

-2 with Ba, Pb, 

Zn, or Cd are evident, even at the low concentrations of these species in the representative 

groundwater composition used in the analysis. Higher concentrations of either species could 

have a marked impact on the distributions reported in Table 3.20, which highlights the fact that 

complex geochemical systems must be fully described to account for all possible interactions. 

3.5.6 Conclusions from Equilibrium Calculations 

Figure 3.51 shows that the most serious water quality issue resulting from CO2 intrusion may be 

the enhanced dissolution of pyrite with consequent solubilization of arsenic, with a high 

probability that the MCL would be exceeded. At plausible P(CO2) levels representative of 

maximum possible values in shallow groundwaters, Ba, Pb and Zn may approach or exceed 

regulatory concentration limits. For those elements, however, model uncertainties, kinetic 

factors, and post-withdrawal water treatment methods all suggest diligence rather than alarm. Of 
 

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the remaining elements, the calculated concentrations of Cd, Hg and Sb are somewhat higher 

than are believed to occur naturally, but analytical methods are insufficiently sensitive for 

accurate quantification. None are expected to exceed MCLs with increasing P(CO2). Hg, Se and 

U concentrations are unaffected by CO2 intrusion. 

From the foregoing discussion and the results depicted in Figure 3.51, it is apparent that of the 

hazardous constituents investigated, only As and to a lesser extent Pb and Zn have the potential 

to exceed MCLs (or SDWRs) through imposition of elevated partial pressures of CO2. The focus 

of subsequent reactive transport simulations in Section 4 is on the mobilization and transport of 

As and Pb in potable water aquifers following intrusion of CO2 from deep storage. 
 

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3.6 Representative Aquifer Host Rock Mineralogies 

3.6.1 Background 

This section provides a basis for defining the mineralogy of representative aquifers exploited for 

potable water, with the objective of illustrating a range of geochemical responses to CO2

intrusion (e.g., a well buffered aquifer vs. a poorly buffered aquifer). The resulting suite of 

realistic “representative” mineralogies are used in the simulation study in Section 4 to evaluate 

the effect of CO2 intrusion on the chemical composition of the groundwater, and particularly on 

the potential changes in the concentrations of hazardous constituents. Our attempt to correlate 

aquifer mineral composition with host rock and groundwater chemical analyses is part of an 

overall strategy to develop a geochemical model capable of representing any potable water 

aquifer. 

Previous modeling of heavy metal mobilization and transport in potable water aquifers assumed 

grossly simplified mineral compositions (Wang and Jaffe, 2004), consisting of 50 wt.% galena 

and 50 wt.% of either calcite or of quartz. In subsequent work, we formulated more realistic 

aquifer mineralogies (Birkholzer et al., 2008; Xu et al., 2007), based on extensive studies of Gulf 

Coast oil field arenaceous aquifers and associated confining shales (Xu et al., 2005), as well as 

on deep carbonaceous sandstone from the Ohio Basin (Zerai et al., 2006; Xu et al., 2006). To 

simulate to potential mobilization of these contaminants under elevated CO2 partial pressures, we 

assumed that a 1% volume fraction of either galena or arsenopyrite was present, in addition to 

the known major minerals. However, as discussed before, these volume fractions of trace 

minerals such as galena or arsenopyrite in potable water aquifer host rocks are still unrealistically 

high. Furthermore, the Gulf Coast aquifer mineralogy, which can be classified as a lithic arkose 

(see Figure 3.53 below), was based on that of deep arenaceous horizons in the Frio Formation of 

Texas, which is not necessarily representative of shallow potable water aquifers over most of the 

contiguous 48 U.S. states. 

Below, we develop “representative” mineralogies for domestic shallow potable aquifers. These 

mineralogies are (1) an impure sandstone typical of those that are found underlying the Atlantic 
 

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Coastal Plain, the Gulf Coast, and deposited in the Central Valley of California; (2) a relatively 

pure arenite, characteristic of the St. Peter sandstone underlying the Illinois basin; and (3) a 

typical dolomitic limestone, found underlying extensive tracts of the Appalachians. These 

mineralogies are a compromise in representing broad ranges of aquifer host rock compositions 

found throughout the United States, with focus on aquifers that may overlie future CO2 storage 

sites and are expected to be vulnerable to CO2 intrusion. This compromise is a necessity in a 

global assessment of groundwater vulnerability, because it would be impossible to explore fully 

the impact on hazardous metals transport under the diverse range of aquifer mineralogies that 

probably exist throughout the United States, even if such mineralogies had been fully 

characterized. 

In practice, such a restriction is not generally a critical issue. The main reason for this is the 

nature of the thermodynamic controls affecting the partitioning of heavy metals between the 

solids of which they form an essential component and the aqueous phase (i.e., groundwater). As 

discussed in Section 3.4.1, equilibrium between the solid and the aqueous phase is governed by 

the relationship specified in Equation (3.1). This relationship can be redefined in terms of 

activity ratios of ionic species or in terms of chemical potentials, both of which are intensive 

parameters, i.e., they are independent of mass. This means that as long as a particular mineral is 

present, it does not matter how much is present. Likewise, if the mineralogy of a host rock is in 

thermodynamic equilibrium, the groundwater composition would not be affected by the relative 

concentrations of the minerals are present. 

Composition-dependent differences may arise, however, upon perturbation of the geochemical 

system, where the reaction path leading to re-establishment of thermodynamic equilibrium is 

defined by chemical kinetic and transport processes. Such a perturbation would arise from CO2

intrusion where, for example, a certain mineral might dissolve entirely, or a secondary mineral 

might precipitate in one aquifer, but would not occur in another containing the same mineralogy, 

but with differing mass fractions. Also, the rate of dissolution or precipitation would be mass- or 

rather surface-dependent. Mass-dependent differences could become important to water quality, 

where pH or Eh buffering minerals are present in low concentrations, or where the activities of 
 

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hazardous inorganic constituents are determined by trace concentrations of minerals containing 

the constituents as essential components. Although such kinetic issues must be recognized in 

simulating aquifer responses to CO2 intrusion, we believe as a general rule that only substantial 

variations in aquifer host rock mineralogy would lead to major differences in the evolution of the 

system due to CO2 intrusion. Thus, selection of the three mentioned mineralogies should cover a 

broad range of likely geochemical environments. Furthermore the methodology developed here 

has flexibility, by allowing simulations to be conducted for site-specific conditions with actually 

measured aquifer mineralogies, physical properties, temperatures, and other relevant properties. 

3.6.2 Approach at Mineralogy Characterization 

3.6.2.1 Distribution of Major and Accessory Minerals 

The Groundwater Atlas of the United States, published by the United States Geological Survey 

(U.S. Geological Survey, 1992-1997), was consulted to identify the extent and importance of 

major aquifer host rock types in the Unites States. The atlas is subdivided into 12 segments, of 

which four were selected for further evaluation: Segment 1 (California, Nevada), Segment 9 

(Iowa, Michigan, Minnesota, Wisconsin), Segment 10 (Illinois, Indiana, Kentucky, Ohio, 

Tennessee), and Segment 11 (Delaware, Maryland, New Jersey, North Carolina, Pennsylvania, 

Virginia, West Virginia). 

Although confined aquifers most likely to be affected by leakage from future underlying CO2

storage reservoirs were of particular interest, the review showed that by far the largest source of 

drinking water in the four identified regions was from unconfined aquifers, which would be less 

likely to retain and trap intruding CO2. In Segments 9 and 10, the sources of many potable waters 

are locally shaley limestone and dolomites, typically less critical with regards to the impacts of 

CO2 intrusion. A host rock mineral composition that is predominantly composed of carbonates 

would mitigate groundwater compositional changes in response to CO2 intrusion because of the 

buffering effect of the carbonates. The main remaining potable water sources in Segment 9 are 

relatively pure sandstones of Cambrian-Ordovician age, principally the St. Peter-Prairie du 

Chien-Jordan, Ironton-Gatesville, and Mount Simon aquifers. In Segment 11, arenaceous 
 

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aquifers of the North-Atlantic Coastal Plain are the principal sources of drinking water, notably 

the Chesapeake, Castle Hayne-Aquia, Severn-Magothy, Peedee-Upper Cape Fear, and Potomac 

aquifers. The predominant source of underground drinking water in the Segment 1 states of 

California and Nevada is the Central Valley Aquifer System. 

Major aquifers vary substantially in mineralogy, both vertically and laterally, reflecting temporal 

and areal facies and depositional changes during palaeogeographical evolution. Furthermore, 

subsequent diagenetic and metasomatic processes also vary in areal extent and intensity, 

depending on postburial history and subsequent exhumation. Thus, the identification of a specific 

aquifer does not result in a unique characterization of its mineralogy. Furthermore, variations 

characteristically overlap with other formations of similar provenance.To characterize the 

mineralogy, we used the principal formations identified from the review of the four segments in 

a series of literature searches, with the objective of identifying mineralogical analyses. Results 

from these searches were disappointing; few papers yielded critical quantitative information of 

the type desired, and those that did were not considered representative. 

After failing to locate suitable representative mineralogical analyses of major aquifers in the 

regions studied, a different approach was taken to characterize the principal mineral composition 

of arenaceous rocks, as discussed in the following paragraphs. Less attention was paid to the 

characterization of carbonate aquifer mineralogy, because the aqueous buffering capacity is 

insensitive to large mass and compositional variations in the essential minerals composing such 

rocks, i.e., calcite and dolomite, and therefore significant mass and compositional variations in 

these minerals are not critical. 

Recently published textbooks on sedimentary petrology (i.e., Greensmith, 1989; Tucker, 1991; 

Blatt, 1992; Boggs, 2003) were reviewed to find a classification that would define the 

mineralogical characteristics of arenaceous rocks in a manner consistent with geochemical model 

development and the objectives of the project. A further requirement was that the classification 

would provide a framework for incorporating and exploring variations in the mineralogy of any 

chosen domestic potable-aquifer host rock. Although the authors of the cited textbooks pointed 

out a long-term problem regarding inconsistent, conflicting, and confusing classification 
 

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schemes, a general consensus has emerged that a scheme broadly based on the classic text of 

Pettijohn et al. (1987) may be most reasonable. 

This scheme by Pettijohn et al. (1987), illustrated in Figure 3.53, as based on Tucker (1991, Fig. 

2.57, p.49), considers only the detrital material comprising the rock, ignoring subsequent postdepositional mineralogical changes arising from diagenesis and metamorphism. The mineralogy 

of the rock “framework,” or large particles, is represented on a triangular diagram in terms of 

three major components: quartz, feldspar, and lithic fragments, while in the plane perpendicular 

to the triangular diagram, the increasing fraction of included “matrix” or interstitial fine-grained 

(<30 μm) material is considered. In further refining a selection of a specific mineralogy for the 

purposes of model development, use was also made of Figure 3.54, also from Tucker (1991, Fig. 

2.62, p. 53), where the petrologic characteristics of arenaceous sediments derived under differing 

tectonic conditions are plotted onto the triangular diagram of the principal framework 

constituents. 

Fig. 3.53. Classification of arenaceous sediments in terms of detrital framework grains (quartz, feldspars and 

lithic fragments) and a detrital (clay) interstitial matrix (after Tucker, 1991). The spot on the 7.5-wt% 

matrix triangle represents the base-case aquifer host rock introduced in Section 3.6.3.1. The diamond 

represents the St Peter Sandstone described in Section 3.6.3.3. For comparison, the star illustrates the 

Gulf Coast Lithic Arkose described in Xu et al. (2006). 
 

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Fig. 3.54. Representation of the tectonic provenances of framework grains of arenaceous rocks (after Tucker, 

1991). TE = trailing edge or passive continental margin; SS = strike slip continental margin; CA = 

converging arc margin; BA = back arc basin and FA = fore arc basin. The black spot indicates the 

base-case aquifer host rock introduced in Section 3.6.3.1. The diamond represents the St Peter 

Sandstone described in Section 3.6.3.3. For comparison, the star illustrates the Gulf Coast Lithic 

Arkose described in Xu et al. (2005). 

After defining the composition of the arenite in terms of the Pettijohn et al. (1987) sandstone 

classification scheme, a further characterization of the basic mineralogy is required. This 

characterization includes (1) identifying the nature and composition of detrital feldspars, (2) the 

composition of the lithic fragments, and (3) the composition of the clay minerals in the matrix. 

Because the provenances of most detrital feldspars were exposed granitic to granodioritic 

batholiths, their composition is presumed to be primarily feldspar and relatively sodic 

plagioclases, i.e., oligoclase. For simplicity, it is assumed that lithic fragments likewise are 

represented by granitic or granodioritic mineralogy. Clay minerals occupy the matrices of 

arenaceous rocks. The clay mineralogy is based on the average clay-mineral composition of 

some shales and mudstones reported in the literature (Boggs, 2003, Table 7.6, p. 289). Table 3.22 

summarizes this mineralogy, and how it is redistributed to conform to limitations in model input. 
 

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Table 3.22. Matrix clay mineralogy. 

Clay Mineral Concentration, 

Wt.% 

Model Distribution, 

Wt.% 

Kaolinite 14.4 22.9 

Smectite 10.7 19.5 

Illite 34.2 40.9 

Chlorite 8.2 16.7 

Mixed-layer illite/smectite 13.0 - 

Kaolinite/chlorite 17.0 - 

Montmorillonite/mixed-layer clays 2.3 - 

Total 99.8 100.0 

The actual characterization of the mineralogy under the three categories listed above is 

somewhat arbitrary, and reflects the lack of critical evaluations necessary to permit 

generalization. Because each clay mineral differs in its site complexation characteristics for each 

hazardous constituent in solution, variations in clay content and composition can markedly affect 

their partitioning between the host rock and migrating pore water in aquifers, and likewise 

strongly affect the mobilization of metals caused by CO2 intrusion, both of which is 

demonstrated by reactive transport modeling in Section 4. It is therefore an important 

consideration that the clay mineralogy be evaluated for any site that becomes a candidate for a 

more detailed evaluation. Through the use of spreadsheets, the mineralogies developed in this 

study can be recalculated for any specific set of site-specific conditions when required. See 

Section 3.6.3 for the four selected mineralogies defined based on the above approach. 

The final step in defining the mineralogy involves the inclusion of minor, albeit critical 

secondary minerals that commonly form during authigenesis and diagenesis, and occasionally 

through epigenesis. These include (1) organic matter (primarily kerogen), (2) carbonates and 

sulfates, both of which act as cement, and (3) iron oxides and sulfides, the former acting as 

adsorbents and the latter acting as hosts for the hazardous inorganic chemical elements that form 

the subject of this study. The concentrations of these constituents in arenaceous sedimentary 

rocks are usually small, and quantitative surveys defining their observed concentration ranges 
 

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have not been made. We therefore estimated suitable values, all of which are probably somewhat 

higher than average, but not unrealistically so. 

We must emphasize that the mineralogical descriptions of the aquifer host rocks, which are 

defined further in the following sections, are based on field observations and are almost never 

assemblages at thermodynamic equilibrium. It is inevitable that incompatible assemblages are 

defined, which can never achieve thermodynamic equilibrium without mineralogical changes. 

Preliminary simulations of aquifer chemistry prior to CO2 intrusion occasionally necessitated ad 

hoc adjustments, and therefore minor differences between the initial and simulation mineralogies 

occur, of necessity to ensure initial steady state, if not thermodynamic equilibrium. 

3.6.2.2 Distribution of Trace-Element Minerals 

The specification of both the identity and the concentrations of trace minerals containing 

hazardous metals as essential components is based on two sources of information: (1) 

identification of the minerals likely to act as hosts through a review of published scientific 

literature bearing on the subject (Section 3.2), and evaluation of potential host minerals 

controlling the saturation concentration of hazardous constituents in NWIS groundwaters 

(Sections 3.3 and 3.4), and (2) specification of their concentrations in soils and sediments 

estimated from information supplied by the National Geochemical Survey (NGS) Database, 

maintained by the United States Geological Survey. This second step is described in the 

following paragraphs. 

The NGS database was searched to find the average concentrations of As, Ba, Cd, Hg, Pb, Sb, 

Se, U, and Zn in all analyzed soils and sediments from the contiguous forty-eight states. Several 

analytical methods are reported in the database. Unfortunately, not all methods actually measure 

the total concentration, but rather a partial amount depending on the effectiveness of treatment 

used in liberating a particular element for the analytical method. In this respect, neutron 

activation is the preferred method, because it does actually measure total concentration. 

(However, not all elements under consideration were analyzed by this method.) The 

concentration, in ppm, was rounded up to the nearest significant figure, and it was then assumed 
 

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that the source rock from which the sediment or soil was derived contains the same concentration 

of that element in a specified mineral where the element is an essential component. The resulting 

concentrations are given in Table 3.23. 

The concentrations of the selected elements were further checked against two other referenced 

sources of elemental abundances; (a) calculated crustal abundances based on the averages of 

values presented by Turekian (1970), Wanke et al. (1984), Weaver and Tamey (1984), Shaw et 

al. (1985), Taylor and McLennan (1985), Ernshaw and Greenwood (1997), Lide (2005), and (b) 

abundances in domestic coals (Kuhn et al., 1981; Golightly and Simon, 2006; Bragg et al., 1998; 

USGS, 2001), both of which are also included in Table 3.23. The latter source is subject to 

deviation from expected norms in potable aquifers, because of the overwhelming content of 

organic matter, which over geologic time could lead to enhanced sequestration of chalcophilic 

elements. However, because most of the elements under consideration are chalcophilic, their 

relative abundances are likely to be minimally fractionated. Furthermore, as noted in Section 3.2, 

an incentive exists for investigating the nature and distribution of hazardous trace elements in 

coals that are discharged into the atmosphere during combustion. As a consequence, many 

minerals containing trace elements have been identified in coal, which are easier to observe and 

characterize than in typical aquifer host rocks. 

With the exception of cadmium and selenium, the elemental abundances characterizing the 

selected elements are generally consistent with corresponding average crustal abundances and 

are therefore believed to be reasonably representative for most aquifer host rocks. The 

assumption that each hazardous element is concentrated in one mineral alone is conservative, 

since each element is expected to be distributed over exchange and adsorption sites in the 

mineral assemblage, co-precipitated with secondary minerals, and in solid solution in primary 

detrital minerals. Indeed, several of the selected constituents may be present entirely in 

substitution as minor components in other minerals, rather being present in minerals listed in 

Table 3.23, where the constituent is an essential component. Under such circumstances, 

calculated equilibrium concentrations in the aqueous phase would represent the expected upper 

limit under reducing conditions. 
 

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Table 3.23. Representation of the mineralogy of hazardous inorganic substances in an arenaceous aquifer host rock 

Concentration, ppm 

National Geochemical Survey Database 

(Mainly Sediments and Soils) 

Element 

ICP Part. 

ICP 

AA NAA NURE 

Average 

Crustal 

Abundance 

(1) 

Coal 

(2) 

Values 

selected 

for this 

Study 

Host Mineral Formula 

As 10 1.3 5.3 10 4.7 1.69 6.8 10 Arsenopyrite 

(SS) 

FeAsS 

Ba 549 - - 500 522 539 237 500 Barite BaSO4

Cd 2 0.09 - 5 5 0.131 0.98 5 Greenockite CdS 

Hg - 0.04 0.03 0.04 500 0.0617 0.17 0.04 Cinnabar HgS 

Pb 18 10 - 20 12 13.9 29 20 Galena PbS 

Sb - 1 0.6 0.5 2 0.2 11.1 0.5 Gudmundite 

(SS) 

Kermesite 

FeSbS 

Sb2S2O 

Se 0.241 0.2 0.25 1 0.067 2.6 0.25 Tiemannite FeSe2

U 100 - - 3 - 1.91 2.3 3 Uraninite UO2

Zn 64 29 100 58 72 832 100 Sphalerite ZnS 

Fe 25400 - - See 

notes 

24,500 50,000 19,600 See 

notes 

Pyrite, 

goethite, 

chlorite, etc. 

FeS2, etc. 

S - - - See 

Notes 

- 468 39,000 See 

Notes 

Pyrite, barite 

greenockite, 

galena, etc. 

FeS2, etc. 

Notes: ICP: Inductively coupled plasma emission spectroscopy 

AA: Atomic absorption spectroscopy 

NAA: Neutron activation analysis 

NURE: National Uranium Resource Evaluation, a program utilizing various analytical methods 

(1): Average of five reported values 

(2): Data from Kuhn et al. (1980), except for Cd (Golightly & Simon, 2006) and Hg (USGS, 2001) 

Concentrations of Fe and S were not selected, as they are defined by the mineralogies specified for the 

aquifer host rocks. 

Both arsenopyrite and gudmundite are expected to be present in solid solution (SS) in pyrite. Greenockite 

might be in solid solution in sphalerite, but it is assumed for the purpose of subsequent reactive transport 

modeling that it is present as a pure substance. 

Note that all representative mineralogies developed in Section 3.6.3 (with the exception of an 

aquifer in oxidizing conditions), utilized for the reactive transport simulation studies in Section 

4, use the same set of trace minerals given in Table 3.23. In other words, we make the 

assumption that trace minerals are present where the hazardous inorganic constituent is either a 

major component or incorporated in solid solution. Based on the discussion in Sections 3.2 and 

3.3.3, the presence of such trace minerals is a reasonable possibility in many aquifers. The 
 

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concentration of these trace minerals, however, are very small, much smaller than was assumed 

in previous studies (Wang and Jaffe, 2004; Birkholzer et al., 2008; Xu et al., 2007). 

3.6.3 Representative Mineralogies 

3.6.3.1 Coastal Plain Aquifer Host Rock 

Most arenaceous sediments of the northeast and southeast Atlantic seaboard, as well as the Gulf 

Coast, fall into the subarkose classification, and are characteristic of trailing edge (TE) or passive 

continental margins (Figure 3.53). Sediments of the Central Valley of California were laid down 

in a fore arc (FA) basin, but their mineralogy is characterized by weathering products of the 

Sierra batholith. A decision was therefore made to choose a framework mineralogy that is a 

compositional compromise between a subarkose and a quartz arenite, consisting of 85 wt.% 

quartz, 10 wt.% feldspar, and 5 wt.% lithic fragments, as identified by the solid circle on Figure 

3.53. Additionally, the clay content in the matrix was arbitrarily chosen to be 7.5 wt.% of the 

total, i.e., the framework comprises the remaining 92.5 wt.% of the rock, intermediate in 

composition between an arenite and a greywacke. 

We then defined the nature and composition of detrital feldspars, the composition of the lithic 

fragments, and the composition of the clay minerals in the matrix. The feldspars were divided 

equally between K-feldspar and oligoclase, reflecting a granite-granodiorite provenance. The 

lithic fragments consist of equal parts quartz, K-feldspar, and oligoclase. The clay mineralogy 

was based on the average clay mineral composition, appropriately modified to account for 

thermodynamic limitations of the model, as described under Section 3.6.2. 

In the final step, the trace minerals defined in Section 3.6.2.2 were added to the basic 

mineralogy. The resulting mineralogy is summarized in Table 3.24. Notice that the expected 

realistic volume fractions of Pb-bearing galena or As-bearing arsenopyrite are far smaller than 

the previously used volume fractions of 50% or 1%, respectively. The specified concentrations 

can be modified, if warranted, to reflect definitive determinations from any specific site. 
 

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3.6.3.2 Coastal Plain Aquifer Host Rock - Oxidizing Conditions 

Many potable water aquifers are unconfined, in which case continuous recharge with oxygenated 

water may eventually lead to oxidation of residual organic matter and secondary sulfides, such as 

pyrite and sulfides containing hazardous inorganic constituents. Furthermore, any ferrous iron 

present in detrital and secondary clays would also be oxidized to the ferric state, although not 

necessarily with any modification to their stability or crystal structure (Fialips et al., 2002). The 

recharge of aquifers by oxygenated groundwaters is a common phenomenon, leading on 

occasion to the formation of a migrating redox front (McMahon and Chapelle, 2008). Pervasive 

in situ oxidation can occur when penetration is rapid, particularly in response to anthropogenic 

extraction of potable water (Thornberg and Sahai 2004) or injection for the purpose of aquifer 

storage and recovery (ASR) (Arthur et al., 2005). Such oxygenation can lead to the mobilization 

of heavy metals, particularly As, Cd, Pb and U, with adverse consequences to water quality. In 

fact, oxidative dissolution may result in a higher degree of heavy-metal dissolution than a 

decrease in pH due to CO2 intrusion. In fact, the oxidative dissolution can lead, under arid 

conditions, to the almost quantitative solubilization of all As present, as the natural abundance of 

As in many rocks, if partitioned completely into the groundwater, would be insufficient to 

achieve saturation with respect to any arsenate minerals after all Pb present has been coprecipitated as mimetite. 

As discussed in Section 3.4, the mineral host limiting Pb concentration in solution under 

oxidizing conditions would be cerussite, which would lead to equilibrium Pb concentrations 

approximately four orders of magnitude greater that under reducing conditions, where galena is 

likely to coexist with pyrite. Because of the likelihood of greatly enhanced solubility of cerussite 

in the presence of elevated CO2 partial pressures, it is considered important to conduct one 

simulation case evaluating the potential behavior of an oxidized aquifer affected by CO2

intrusion. 

Table 3.24 contains a listing of a modified mineralogy of a representative Coast Plain Aquifer 

host rock, where all minerals likely to buffer the redox state under reducing conditions have been 

eliminated, and only cerussite is present as hazardous inorganic host mineral. 
 

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3.6.3.3 St Peter Sandstone 

The principal arenaceous aquifer rocks of Segments 9 and 10, e.g., the St Peter Sandstone, are 

reworked sands associated with a relatively stable cratonic environment, virtually devoid of 

residual feldspathic or lithic fragments and containing only a very small percentage of clay 

matrix. They therefore fall near or within the classification of quartz arenite (Figure 3.53). 

Because of their limited buffer capacity, extensive distribution, and the fact that they overlie 

much of the Illinois Basin, which is a likely candidate for the storage of CO2 in deep underlying 

saline aquifers, it was decided that a representative mineralogy for the St Peter’s Sandstone 

should be formulated for the reactive transport modeling purposes. Mineralogical data, cited in 

the literature for St Peter Sandstone in Illinois (Thiel, 1935; Odom et al., 1977; Hoholick, 1980; 

Hoholick et al., 1984; Barnes et al., 1992; Girard and Barnes, 1995; Pitman et al., 1997) were 

used to develop the representative mineralogical composition as listed in Table 3.24. Noteworthy 

is the addition of dolomite to the carbonate mineralogy. Otherwise, the minerals present are 

similar, but in significantly differing proportions to those of the Coastal Plain Aquifer. The 

framework mineralogy selected for St Peter Sandstone falls within the pure arenite field, as 

indicated by the diamond symbol in Figure 3.53. The matrix mineralogy consisting of 

carbonates, clays, goethite, pyrite and kerogen, in total, comprise only 2 wt.% of the rock, of 

which clays constitute about 1 wt.%. The specific mineral hosts for the hazardous inorganic 

constituents were retained with the concentrations as those specified in Section 3.6.1.2. 

3.6.3.4 Dolomitic Limestone 

Wang and Jaffe (2004) pointed to the fact that the presence of carbonates tends to buffer the pH 

of the groundwater, even in the presence of elevated CO2 partial pressures up to 10 bars. 

However, even though most arenaceous aquifers contain traces to minor concentrations of 

carbonates (principally calcite), the continuous ingress of CO2, leaking from an underlying 

storage reservoir, could eventually dissolve all the calcite in the vicinity of the point of ingress. 

With the buffering capacity gone, the pH would fall and further enhance the solubility of 

minerals containing hazardous inorganic constituents as essential components. 
 

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Many potable groundwaters are drawn from aquifers consisting predominantly of limestones, 

some quite shaley or dolomitic, especially in regions aligned with the west flanks of the 

Appalachians and the Cumberland Plateau of Tennessee, such as the upper Knox Limestone. 

Representative dolomitic limestones contain up to 20 wt.% dolomite. In our study, the 

mineralogy of an impure dolomitic limestone was formulated to represent the extreme case 

where complete dissolution due to (even long-term) ingress of CO2 would be implausible. The 

clay composition of the dolomitic limestone differs significantly from those of the other rocks 

specified for study, not only in quantity, but also in kind. Of the total clay concentration of 16 

wt.%, 7 wt.% is illite and 5wt.% is kaolinite, chlorite and smectite making up the balance in 

equal proportions. The clay mineralogy is dictated partly by the diagenetic history of dolomitic 

limestone, where it is assumed that both dolomitization and illitization of smectitic clays were 

the result of thermogenic processes caused by deep burial. The contrasting clay mineralogy 

impacts the adsorption of hazardous constituents, as adsorption sites on illite differ from those of 

smectite in magnitude and character, which is demonstrated in Section 4.7.2. This mineralogy is 

given in Table 3.24. The specific mineral hosts for the hazardous inorganic constituents were 

retained with the concentrations of those specified in Section 3.6.2.2. 
Table 3.24. Representative mineralogies of aquifer host rocks used in reactive chemical transport simulations 

Coastal Plain Aquifer (Arenite) Reducing Env. 

St Peter Sandstone Dolomitic Limestone Coas

Oxid

Mineral Element gfw V, cm3/mol Wt. % Vol Fraction Wt. % Vol Fraction Wt. % Vol Fraction Wt. % Quartz 60.084 22.688 80.165 0.77666 92.756209 0.926778 5.009065 0.05144173 79.28K-feldspar 278.33 108.87 6.165 0.06187 4.881906 0.0505281 0.000000 0.00000000 6.097Oligoclase 1327.094 502.477 6.175 0.09998 0.244095 0.00244551 0.000000 0.00000000 6.107Kaolinite 258.159 99.52 1.72 0.01701 0.097638 0.00099595 5.009065 0.05251716 1.701Smectite-Ca 365.394 132.51 1.46 0.01358 0.244095 0.0023423 2.003626 0.01976175 1.443Illite 383.899 138.94 3.07 0.0285 0.390552 0.00374012 7.012692 0.0690266 3.036Chlorite 634.653 210.26 1.25 0.0106 0.195276 0.00171185 2.003626 0.01805338 0.000Kerogen-OS 1881.97 1738.37 0.1125 0.002666 0.097638 0.00238641 1.001813 0.02516736 0.000Pyrite 119.977 23.94 1 0.00512 0.097638 0.00051551 0.500907 0.00271834 0.000Dolomite 184.40 64.365 0.000000 0.000 0.488191 0.00450893 15.027196 0.14265542 0.000Calcite 100.087 36.934 1.6 0.01514 0.097638 0.00095337 60.108785 0.60326492 0.988Goethite 88.852 20.82 1 0.0601 0.097638 0.00060538 2.003626 0.01276885 0.988Apatite P 502.31 158.957 0.270287 0.0021945 0.263904 0.00220978 0.270778 0.00233047 0.267Barite Ba 233.39 52.1 0.08496 0.00048944 0.027654 0.00016334 0.028374 0.00017227 0.084Arsenopyrite As 162.83 26.272 0.002100 9.0484E-06 0.002122 9.0594E-06 0.002177 9.5542E-06 0.000Greenockite Cd 144.48 26.42 0.000621 3.0327E-06 0.000628 3.0363E-06 0.000644 3.2022E-06 0.000Cinnabar Hg 232.66 28.416 0.000004 1.4622E-08 0.000005 1.4640E-08 0.000005 1.5439E-08 0.000Galena Pb 239.27 31.49 0.002232 7.8433E-06 0.002255 7.8529E-06 0.002314 8.2817E-06 0.000Cerussite Pb 267.21 31.49 0.000000 0.00000000 0.000000 0.00000000 0.000000 0.00000000 0.002Gudmundite Sb 121.75 73.352 0.000024 3.8866E-07 0.000068 3.8913E-07 0.000070 4.1038E-07 0.000Tiemannite Se 213.77 30.003 0.000033 1.2256E-07 0.000033 1.2271E-07 0.000034 1.2941E-07 0.000Uraninite U 270.03 24.616 0.000329 8.0056E-07 0.000332 8.0154E-07 0.000341 8.4531E-07 0.000Sphalerite Zn 96.98 23.83 0.014335 9.4066E-05 0.014485 9.418E-05 0.014862 9.9324E-05 0.000Total - - - 100. 1 100 1 100 1 100 
 

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3.7 Summary Results and Implications for Reactive Transport 

Model of CO2 Intrusion 

Section 3 of this report covers the development of necessary input for the reactive chemical 

transport simulations of high-pressure CO2 intrusion into shallow potable water aquifers, which 

are described in Section 4. A comprehensive geochemical model was developed representing 

typical conditions in many freshwater aquifers in the United States. An important step in 

developing this geochemical model was to use a distribution-of-species code, EQ3/6, for the 

evaluation of the thermodynamic controls influencing the initial aqueous concentration and CO2-

related mobilization of selected hazardous inorganic constituents. By evaluating the chemical 

analyses of more than 38,000 groundwaters from the USGS NWIS database, minerals hosts were 

identified containing one or more of the hazardous constituents, either as essential components or 

in solid solution. These hosts, upon equilibration with groundwater, are able to assume partial or 

complete control of the aqueous trace-metal concentrations and their changes upon ingress of 

CO2, depending on the nature of the mineralogy of the aquifer host rock. A representative traceelement mineralogy was developed for the solubility-controlling minerals identified above, 

including their expected (typically very small) concentrations in the aquifer solid phase, and was 

subsequently used for the reactive transport model in Section 4. 

Identification of thermodynamic controls necessitated the development and testing of a large 

supporting thermodynamic database comprising the solubility products of minerals of interest, 

and of complexes that could affect the concentrations of the hazardous constituents in aqueous 

solution. In addition, the bulk mineralogy of the aquifer host rocks also had to be specified, 

which was accomplished through review of the scientific literature. The bulk mineralogy was 

combined with the trace element mineralogy involving the hazardous inorganic constituents, 

whose abundance were defined in terms of the natural abundances of the hazardous constituents 

in soils and sediments in the United States. The integrated mineralogy was normalized in terms 

of molar volume fractions and passed through for TOUGHREACT simulations described in 

Section 4. In addition, thermodynamic data, in the form of mineral solubility products and 
 

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aqueous complex dissociation constants used in the database of EQ3/6, were also passed through 

and formatted for the corresponding TOUGHREACT database. 

Prior to the complex reactive-transport modeling in Section 4, a preliminary assessment of CO2-

related water quality changes was conducted by calculating the expected equilibrium 

concentrations of hazardous inorganic constituents as a function of the partial pressure of CO2 in 

water with a typical potable groundwater composition, defined based on the above geochemical 

evaluation. Results indicate that the concentrations of some of the constituents are quite sensitive 

to changes in P(CO2), whereas others remain relatively unaffected. This assessment was used in 

Section 4 to focus the reactive transport simulations on those constituents that have the clear 

potential to exceed maximum contaminant levels through imposition of elevated partial pressures 

of CO2. 

The foregoing paragraphs cannot do justice in summarizing the many tasks that formed part of 

the overall geochemical evaluation in Section 3. Nor can they touch on the assumptions, 

approximations, and compromises that were necessary to bring such a complex study to fruition 

in the time available. In the following paragraphs, we summarize in greater detail the several 

tasks accomplished in Section 3 of this report, and indicate further steps that would be desirable 

in refining the geochemical modeling with the objective of quantifying and decreasing current 

uncertainties. These tasks are described in the following order: (1) literature review providing 

preliminary identification of thermodynamic controls for hazardous inorganic constituents, (2) 

review and modification of an extensive thermodynamic database for relevant minerals and 

aqueous species and site complexation constants, (3) evaluation of more than 38,000 

groundwater quality analyses from potable water aquifers throughout the United States, with 

final specification of solubility-controlling minerals for hazardous inorganic constituents, (4) 

evaluation of the natural abundances of these hazardous constituents in sedimentary formations 

hosting potable water, (5) determination of typical aquifer host-rock mineralogies, and (6) 

calculation of equilibrium concentrations as a function of partial pressure of CO2. 
 

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(1) Preliminary Assessment of Solubility-Controlling Minerals via Literature Review 

Thermodynamic equilibrium of a given hazardous inorganic constituent can be established 

between the aqueous phase and (a) adsorption sites on mineral surfaces (e.g., iron oxides, 

silicates, sulfides, carbonates, etc.), (b) on functional groups on organic matter, (c) by ion 

exchange sites in clays, zeolites, etc., (d) in solid solution in primary and secondary minerals, 

and (e) in discrete minerals where the element in question is an essential component. Because of 

the extremely low solubilities of many of the minerals falling under Category (d) and (e), such 

minerals are most likely to act as the primary thermodynamic controls determining the 

concentrations of the constituents in groundwaters. Not many studies are reported in the 

literature that would help identify such solubility-controlling minerals for the relevant hazardous 

constituents and the range of geochemical conditions observed in United States aquifers. We 

therefore based our initial assessment of possible minerals controlling aqueous concentrations in 

groundwater on the extensive literature describing the mineralogy of coals and marine sediments 

(Section 3.2). (For the final assessment, we conducted a rigorous thermodynamic evaluation of 

more than 38,000 groundwater analyses from potable aquifers throughout the United States, 

using the distribution-of-species code, EQ3/6.) 

Because coal is burned, and hazardous inorganic constituents can be released in volatile or 

particulate form to the atmosphere, there is considerable interest in the mode of occurrence of 

such constituents, and several quite elaborate studies have been undertaken over the past 40 

years to establish where and how they are distributed in coal. (Although coal can hardly be 

considered representative of aquifer host rocks, its component parts, which crudely classify the 

potential host materials listed above, are found in both, although their respective mass fractions 

differ greatly.) The chemistry of each component has been determined through application of a 

sequential procedure using progressively more aggressive lixiviants, supplemented by suitable 

analytical and diagnostic procedures to characterize the elemental and mineralogical composition 

together with microscopic techniques. A limited number of similar studies have also been 

undertaken to examine the sedimentology of surficial marine sediments during authigenesis, the 

goal being to determine the immobilization of heavy metals through reduction and co-
 

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precipitation with pyrite. Finally, the descriptive literature on the metallography of polymetallic 

ore deposits is a helpful adjunct in identifying anticipated phase and solid solution relationships 

among ore minerals, especially those most likely to form in minute and usually undetectable 

concentrations in potable water aquifers. 

Based on the literature review, a list of potential mineral hosts was generated for the selected 

hazardous inorganic constituents and average geochemical conditions. This list was used as a 

suitable starting point for the rigorous thermodynamic evaluation conducted in a second step (see 

Task (3) below). 

(2) Modification and Augmentation of Thermodynamic Data 

The geochemical models used in the current study require solubility products of solid phases and 

dissociation constants for complexes. Both parameters are defined by equations relating the solid 

phases or complex to basis species in solution. The respective solubility products and complex 

dissociation constants can be calculated as a function of temperature and pressure, provided that 

sufficient thermodynamic parameters are available. To ensure a realistic thermodynamic 

description of the complete geochemical system, we reviewed the available literature and, if 

necessary, revised and augmented the thermodynamic databases associated with EQ3/6, the 

distributions-of-species code, and TOUGHREACT, the reactive transport simulator. This process 

was conducted for the hazardous inorganic constituents, as well as for a variety of additional 

rock forming and accessory minerals (Section 3.4.2). 

In many cases, changes to the existing thermodynamic databases for EQ3/6 and TOUGHREACT 

were necessary not only to ensure that the geochemical model realistically represented aquifer 

conditions, but also to eliminate obsolete and/or erroneous data. Most importantly, several 

significant errors and omissions were discovered that entailed a complete revision of mineral 

solubility and aqueous sulfide and selenide complexation data for Cd, Hg, Pb and Zn. The 

revision was undertaken in an expedient, though not completely rigorous manner. The raw data 

from a substantial number of refereed papers related to sulfide and selenide mineral solubilities 

will eventually require a complete re-analysis to meet archival standards. Such a re-analysis, 
 

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however, is not expected to significantly impact the findings in this report. All changes and 

revisions in the thermodynamic databases are described in great detail in Appendix C. 

For many of the minerals and some of the aqueous species, the SUPCRT92 code was used to 

calculate solubility products and complex dissociation constants as a function of pressure and 

temperature from given thermodynamic parameters. However, some constants were also taken 

directly from the literature, most of which were specified at 25oC and 1 atmosphere pressure. 

Because of the magnitude of the task in compiling data for all the constituents under study, 

certain approximations and assumptions were made as documented in Appendices C, D, and E, 

which could introduce minor inconsistencies. These inconsistencies are believed to be small, and 

well within the overall uncertainty of the model. 

The modified and augmented thermodynamic database was used in Section 3.4.5 to evaluate the 

groundwater quality data from the NWIS database and to identify minerals that control the 

activities of the selected hazardous constituents (see summary below). It was observed that some 

solubility-controlling minerals exhibited moderate discrepancies between the ideal saturation 

index value of zero and the calculated modes of the 38,000 chemical analyses. This closing error 

between the actual field observation and the thermodynamic model prediction was reconciled by 

further adjusting the log K values of the dissolution constants so that the calculated mode for 

each hazardous constituent was shifted to zero (Section 3.5.2). This was done in both the EQ3/6 

and the TOUGHREACT databases prior to modeling the geochemical impact of CO2 intrusion. 

In other words, a geochemical model calibrated to the NWIS groundwater analyses was used for 

the equilibrium calculations conducted in Section 3.5 as well as for the reactive transport 

simulations conducted in Section 4. 

(3) Evaluation of Groundwater Quality Data from the NWIS Database and Identification 

of Thermodynamic Controls 

Over 38,000 groundwater analyses were retrieved from the NWIS database, with the main 

objective of identifying thermodynamic controls that govern the concentrations of hazardous 

inorganic constituents in groundwaters. Prior to conducting the geochemical calculations using 
 

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the distribution-of-species code, EQ3/6, the concentration distributions of these constituents were 

examined for artifacts caused by the use of insufficiently sensitive analytical methods. In some 

cases, these resulted in spurious distributions in concentrations of elements whose ambient 

concentrations were near or at the limits of detectability. This applied in particular to Cd, Pb, Sb, 

and possibly Hg. The analytical methods and their ability to measure constituent concentrations 

in groundwaters are listed in Appendix A. A discussion of the analytical artifacts introduced by 

the use of insufficiently sensitive analytical methods is given in Appendix B2. 

A preliminary examination of all water quality analyses was also conducted to assess analytical 

integrity. This examination showed that the majority of analyses possessed charge imbalances 

that were comparably large according to recognized standards. Although the principal cause 

could be attributed to inadequate custodial handling, leading to sample CO2 degassing and 

probable precipitation of carbonate, no single correction method could be identified that would 

eliminate these errors without introducing others. Therefore all analyses were used in subsequent 

geochemical evaluations, but with due consideration being paid to the limitations of the data. 

A major challenge with the evaluation of the water quality analyses was finding a way to define 

the redox state of the groundwaters, given the absence of reliable field measurements for this 

parameter. Two approaches were taken: (1) Eh was calculated based on the Fe(III)/Fe(II) couple 

at a given pH, using analytical data for dissolved iron together with the assumption that the 

groundwater was saturated with respect to goethite, and (2) Eh was defined at a given pH by the 

pyrite/goethite buffer. Two sets of runs were conducted with EQ3/6, in which the more than 

38,000 analyses were speciated assuming the calculated Eh. The calculated saturation indices 

were the examined with respect to all those minerals containing a hazardous constituent as an 

essential component. Runs were conducted at 25oC and 1 atmosphere pressure rather than at 

ambient temperatures (which were probably lower), because the NWIS data did not report the 

ambient temperature and because some of the thermodynamic data used in the databases of the 

code was specified only at 25oC. This assumption introduces an uncertainty in the results, which 

is smaller than the overall uncertainty associated with the water quality analyses and smaller than 

the natural variability in the large population of groundwaters studied. 
 

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Several iterative refinements and augmentations to the thermodynamic data were made in 

successive runs, during which obvious errors were identified and tracked down. In total, 

approximately one million species distributions were made. The results of the analysis turned out 

to be in good agreement with the data, giving a strong indication that the model assumptions are 

robust. Solubility-controlling minerals were identified with reasonable confidence where the 

predicted saturation indices of many of the candidate host minerals were close to or at zero, and 

the identified minerals correlated well with the limited quantity of information regarding their 

occurrence in the field, especially with respect to the coal data. Results were also consistent with 

the detectability of the hazardous constituents in groundwaters as determined by the analytical 

method used and its sensitivity. 

Perhaps the most important observation stemming from the analysis using two methods of 

independently computing the groundwater Eh was that average mineral solubilities were 

consistent with conditions close to or defined by the pyrite/goethite buffer, thereby indirectly 

substantiating the argument that the majority of groundwaters comprising the data set examined 

are moderately to strongly reducing. Only a small number of analyses, such as those consistent 

with cerussite saturation, are indicative of more oxidizing conditions. The information derived 

from the database analysis also showed that the average pH of all groundwaters was about 7.6, 

and that the calculated average ambient Log P(CO2) was about –2.5, one unit higher than that of 

the atmosphere, and that most aquifers tested were saturated with respect to calcite. 

For a representative groundwater at reducing conditions, the most likely mineral hosts 

controlling the concentration of the hazardous inorganic constituents studied are as follows. 

Arsenic is present as the arsenopyrite component in solid solution in pyrite. Ba is saturated with 

respect to barite. Cd is either in solid solution in sphalerite and/or is present as greenockite or 

cadmoselite. Hg is present as cinnabar and possibly as tiemannite, but its presence in solid 

solution in pyrite, perhaps in minor amounts, cannot be ruled out. Pb is present as galena, and/or 

possibly as clausthalite. Sb is present as the gudmundite component in solid solution in pyrite, 

and probably as kermesite, especially in neutral to acid conditions. Se could be present as either 

clausthalite, tiemannite, or cadmoselite. U is present as uraninite and/or coffinite. and zinc is 
 

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present as sphalerite and/or hemimorphite. Some of the uncertainty associated with the specific 

identification of one particular mineral can be attributed to variations in the redox potential and 

pH between aquifers (owing to the presence of differing redox buffers from the standard), 

variations in the absolute abundances of the hazardous constituents in the aquifer host rocks, and 

the fact that the Gibbs Phase Rule with zero degrees of freedom at the specified pressure and 

temperature would not be violated in some cases were both cited minerals to be present. 

The mean saturation indices of some of the mineral hosts, identified as controlling the 

concentrations of certain hazardous inorganic constituents, deviate positively from zero. 

Examples include uraninite and coffinite for uranium, and sphalerite and/or hemimorphite or 

zinc. Such deviations could be caused by any of a number of reasons relating to the 

thermodynamic properties of the participating solid phases and aqueous species in solution, to 

systematic deviations in the calculated redox potential, or to differences between the reference 

temperature and mean formation temperature. Further targeted investigations are warranted to 

identify the underlying causes of the saturation differences between calculated and field 

measurements (as defined by the saturation index), and to decrease ambiguity in a few cases. 

Where the mean saturation indices of some of the mineral hosts deviate negatively from zero, it 

is likely that a solid solution may exist in another mineral host. For example, arsenopyrite, 

gudmundite, or dzarkenite (ferroselite) components can represent As, Sb, and Se substitution in 

pyrite. The geochemical model does not currently account for such solid solutions, and therefore 

the magnitude of such occupancies cannot be directly calculated. Therefore, approximations 

were made in the current study to offset the omission of a solid solution model for arsenopyrite 

and gudmundite. Future model refinements would enhance the overall accuracy of geochemical 

simulations. 

The dispersion revealed in the saturation index histograms for the minerals displayed in Section 

3.4.4, 3.4.5, and 3.4.6 are a measure of the total uncertainty and variability of the analyzed 

systems. Uncertainties in geochemical model parameters, as well as the natural variations 

expected in groundwaters of differing provenances, contribute to this dispersion. A systematic 

examination of the uncertainties of each parameter, and their impact on the overall dispersion of 
 

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specific mineral saturation indices, is feasible but time consuming. Such an analysis would 

provide much insight as to what extent each parameter contributes to geochemical model 

uncertainty. The evaluation presented in this report can therefore be a point of departure for the 

study of general issues relating to the validity of geochemical models in both a scientific and 

regulatory environment. 

We emphasize that the approach of determining thermodynamic controls chosen in this study is 

an indirect one; it does not provide a direct confirmation of the existence of the minerals hosting 

the inorganic constituents. Direct confirmation is achievable only through verification of their 

presence through mineralogical, spectroscopic, or separation procedures. Such procedures are 

extremely challenging, because the quantities present are minute, and particle sizes may be 

extremely small. We recommend conducting such procedures in future site-specific studies to 

demonstrate the validity of the general geochemical model developed here. 

(4) Natural Abundances of the Trace Elements of Interest 

The reactive transport model utilized in Section 4 requires specification of the volumetric 

fractions of the solubility-controlling mineral hosts for hazardous inorganic constituents 

identified above. It is important that the specified concentrations of these hazardous constituents 

in the aquifer host rocks are representative of those found in the United States. A search of the 

literature showed that such information was not generally available, and therefore use was made 

of the National Geochemistry Database giving average elemental abundances in surficial soils 

and sediments in the United States, to act as a proxy for abundances in aquifer host rocks. 

Several alternative analytical methods were used in the compilation of the National 

Geochemistry Database, each having differing detection levels and characteristics. Average 

values were selected from among those different analytical methods deemed most representative. 

A comparison was also made between the selected average concentrations and the average 

crustal abundances of those same elements reported in the literature, and it was found that they 

were for the most part comparable. Average abundances of hazardous inorganic constituents 

ranged from < 10-7 for Hg to >10-4 for Ba (Section 3.6.2.2). While the above abundances are very 

small compared to the major minerals, all hazardous constituents targeted for study in this report 
 

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occur ubiquitously in soils, sediments, and aquifer rocks throughout the world, indicating that the 

problem of mobilization of these constituents in response to CO2 intrusion can be a widespread 

one in United States aquifers (Section 3.3.2). 

(5) Typical Aquifer Host Rock Mineralogies 

The mineralogy of the host rock of a potable water aquifer can have a significant influence on 

the water quality, and similarly impact the nature of compositional changes due to the ingress of 

high pressure CO2. In this report, one goal was to characterize the mineralogies of a selected 

group of host rocks that would be considered representative of the majority of confined and 

unconfined potable water aquifers in the United States. Another consideration was that the 

selected aquifers should also be those most likely to be affected by intruding CO2 from deep 

saline storage reservoirs. At the outset of the study, it was presumed that the mineralogies of 

candidate aquifers would be sufficiently well characterized that the appropriate information need 

only be lifted from suitable literature sources. It turned out that such information is not widely 

available, nor is it of sufficient quality or detail to be used for the simulations contemplated in 

this study. Representative mineralogies therefore were created using standard sedimentological 

classifications, and these were augmented with the trace quantities of the minerals identified as 

being at equilibrium with the selected hazardous inorganic constituents in the NWIS 

groundwaters (Section 3.6). The specification of different mineralogical compositions was 

facilitated by the utilization of ExcelTM spreadsheets. If necessary, site-specific mineralogies can 

be entered and suitable inputs for the TOUGHREACT or other simulators generated to suit 

specific needs. 

The created mineralogical assemblages, when contacted by the aqueous phase, are not 

necessarily able to come to equilibrium without progressive destruction of one or more minerals, 

because the mineralogy is commonly overspecified, and the Gibbs phases rule is violated. 

However, this condition is also typical of real sedimentary assemblages and is characteristically 

responsible for observed authigenic and diagenetic processes. Therefore, the modeled 

groundwater composition generated after attempting a simulated equilibration in reactive 

transport modeling invariably represents a quasi- rather than true equilibrium state. Despite this 
 

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limitation, the simulated groundwater composition with specified initial concentrations of 

conservative components, such as chloride ion and Cl-

, should fall within the range of 

groundwater compositions downloaded from the NWIS database (see Section 4.4). 

(6) Concentrations of Hazardous Inorganic Constituents as a Function of P(CO2)

A preliminary assessment was made of how the partial pressure of CO2 affected the equilibrium 

concentrations of hazardous inorganic constituents in water with a typical potable groundwater 

composition, assuming reducing conditions defined by the pyrite/goethite buffer (Section 3.5). 

The assessment incorporated both sulfide and selenide complexing of chalcophilic elements. Log 

P(CO2) was varied between –4 and +1, leading to a corresponding pH variation between 8.9 and 

5.6. Of the nine hazardous constituents studied, only As, and to a lesser extent Pb and Zn, are of 

have the potential for exceeding maximum contaminant levels at elevated P(CO2). In Section 4 

of this report, primary emphasis is placed on the evaluation of the response and transport of As 

and Pb under intruding CO2. 
 

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4. Systematic Geochemical Modeling 

This section describes a systematic modeling study conducted with a reactive transport simulator 

to quantify the effect of CO2 intrusion into a shallow aquifer. The goal was to determine the 

potential amount of hazardous constituents that can be mobilized by the ingress of CO2, to 

predict the fate and migration of these constituents in the groundwater, and to evaluate the 

likelihood that drinking water standards might be exceeded. A variety of scenarios and aquifer 

conditions was considered in a sensitivity evaluation. These scenarios and conditions, in 

particular those describing the geochemistry and mineralogy of potable aquifers, have been 

selected based on the geochemical model definition described in the previous section. 

4.1 TOUGHREACT Simulator 

We conduct our geochemical modeling with TOUGHREACT, a simulation program for 

nonisothermal multiphase reactive transport in variably saturated media (Xu et al., 2006). 

TOUGHREACT has been widely applied to many geologic systems and environmental 

problems, including geothermal systems, diagenetic/weathering processes, subsurface waste 

disposal, acid mine drainage remediation, contaminant transport, and CO2 geologic storage (e.g., 

Xu et al., 2005). The simulator was developed by introducing reactive chemistry into the existing 

framework of a nonisothermal, multiphase, multicomponent fluid and heat flow code (Pruess et 

al., 1999). 

TOUGHREACT considers a wide range of thermo-physical-chemical conditions of pressure, 

temperature, water saturation, ionic strength, pH, and Eh. The major processes for fluid and heat 

flow are: (1) fluid flow in liquid and gas phases under pressure, viscous, and gravity forces, (2) 

interactions between flowing phases represented by characteristic curves (relative permeability 

and capillary pressure), (3) heat transfer by conduction and convection, and (4) diffusion of 

water vapor and noncondensable gases. Transport of aqueous and gaseous species by advection 

and diffusion is considered in both liquid and gas phases (Xu et al., 2005; 2006). 
 

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A specific fluid property module within TOUGHREACT for CO2 sequestration includes a 

comprehensive description of the thermodynamics and thermophysical properties of a H2O–NaCl 

-CO2 system. Flow processes can be modeled isothermally (as done in this study) or 

nonisothermally, and phase conditions may include a single (aqueous or CO2-rich) phase, as well 

as two-phase mixtures. Depending on the temperature-pressure conditions, CO2 may be in 

supercritical or gaseous form. Aqueous chemical complexation and gas (CO2) 

dissolution/exsolution are assumed to be at local equilibrium. For the conditions assumed in this 

study, the intrusion of gaseous CO2 into a freshwater aquifer is modeled as a two-phase flow 

system, with dissolution of CO2 into water occurring instantaneously until equilibrium 

concentration is reached. Excess CO2 migrates within the aquifer, driven mainly by buoyancy 

forces. 

The geochemical model implemented in TOUGHREACT considers a variety of equilibrium and 

kinetic chemical reactions (Xu et al., 2005; 2006). Aqueous complexation, acid-base, redox, and 

cation exchange are considered under equilibrium assumptions. The activity of aqueous species 

is equal to the product of the activity coefficient and the molal concentration. Aqueous species 

activity coefficients with the exception of CO2(aq) are calculated from the extended DebyeHückel equation (Helgeson et al., 1981). The CO2(aq) activity and CO2(gas) fugacity coefficients 

are determined as functions of pressure, temperature, and salinity, with details given in Xu et al. 

(2004). The thermodynamic data incorporated in TOUGHREACT have been derived from a 

comprehensive review of the published literature. Originally based on the equilibrium constants 

for aqueous species and minerals given in the EQ3/6 V7.2b database (Wolery, 1992), many 

substitutions and changes to this data base have been incorporated into TOUGHREACT over the 

years in response to revised or improved information on the given thermodynamic properties (Xu 

et al., 2005, 2006). In addition to these changes, Xu et al. (2005) have incorporatedthe 

thermodynamic properties of several phases relevant to geologic sequestration of CO2, among 

these various carbonate phases. Mineral dissolution and precipitation can be modeled in 

TOUGHREACT, subject to either local equilibrium or kinetic conditions. For kinetically 

controlled mineral dissolution and precipitation, a general form of rate law is used, based on that 

used by Steefel and Lasaga (1994). The TOUGHREACT version with the geochemical model 
 

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described in Xu et al. (2005; 2006) was the starting point for the modeling studies performed in 

this study. However, further modifications were made, as briefly summarized below. 

4.1.1 Thermodynamic Data and Kinetic Data 

Because of the unusual requirements of the current study, the thermodynamic data incorporated 

within the TOUGHREACT version described in Xu et al. (2005; 2006) are insufficient for 

adequately modeling the complex chemistry associated with the behavior of hazardous inorganic 

constituents in groundwaters. Therefore, the available literature was reviewed and the database 

used for the reactive transport simulation has been significantly modified and, if necessary, the 

thermodynamic database in TOUGHREACT was revised and augmented within this study, as 

described in Section 3.4.2 and Appendix C. Specific consideration was given to the 

thermodynamic data of the metal sulfide minerals and aqueous complexes (see Appendix D for 

details), as well as metal selenide minerals and aqueous complexes (see Appendix E). Further 

modification of thermodynamic parameters was necessary to reconcile differences between the 

measured and predicted solubility controls in the more than 38,000 groundwater analyses from 

the NWIS database. The modifications made to “calibrate” the geochemical model are described 

in Section 3.5.1. 

Mineral dissolution and precipitation rates are a product of the kinetic-rate constant and the 

reactive surface area (Xu et al., 2006). Mineral reactive surface areas (except those for illite, 

kaolinite, smectite and goethite) used in this study are taken from Xu et al. (2006, 2007), which 

are based on the work of Sonnenthal et al. (2005). The values developed in Xu et al. (2006, 

2007) are typically two orders of magnitude smaller than measured surface areas based on 

surface roughness evaluation, because only part of the total mineral surface is involved in the 

chemical reaction, due to coatings, reduced exposure to the aqueous phase, and channeling of 

reactive flow. The reactive surface areas of illite, kaolinite, smectite and goethite are taken from 

published BET surface areas (Bradbury and Baeyens, 2005; Gu and Evans, 2007; Lackovic et al., 

2003; Muller and Sigg, 1991) in conformity with the surface area used for surface complexation 

reaction involved in adsorption/desorption. Kinetic rate parameters for most minerals are taken 

from Xu et al. (2006, 2007), which are based on work by Palandri and Kharaka (2004), with the 
 

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exception of galena and arsenopyrite. Kinetic rate parameters for galena are taken from Zhang et 

al. (2003). Those for arsenopyrite are assumed to be the same as those for galena, based on their 

structural similarity. All parameters are given in Table 4.3 and other tables throughout Section 4 

that detail the basic mineralogies used in the simulations. The impact of uncertainy in the kinetic 

rate parameters has been evaluated in a sensitivity analysis (Section 4.6.2). 

4.1.2 Improved Adsorption Models 

Adsorption via surface complexation has been widely studied (Dzombak and Morel, 1990; 

Lutzenkirchen, 2006) and is a key process with respect to the fate and transport of heavy metals 

(e.g., Bradl, 2004) or other hazardous constituents such as As (Goldberg, 2002; Goldberg et al., 

2005; Manning and Goldberg, 1997). We have therefore incorporated into TOUGHREACT a 

model considering adsorption via surface complexation, using nonelectrostatic, constant 

capacity, and double layer models (Dzombak and Morel, 1990). The surface complexation model 

implemented in TOUGHREACT was verified by comparison with other codes such as 

PHREEQC (Parkhurst and Appelo, 1999) and CRUNCH (Steefel, 2001). 

Adsorption of heavy metal (and As) ions on minerals is influenced by a variety of factors, the 

most important of which are pH, type, and speciation of metal ion involved, as well as 

competition among different ions (Bradl, 2004). In this study, we consider goethite, illite, 

kaolinite, and smectite as principal adsorbents, mainly because these minerals have strong 

adsorption capacities with respect to Pb and As. A literature analysis was conducted to determine 

relevant surface-complexation reactions and their thermodynamic constants for these cations and 

minerals, as summarized in Appendix F. 

Usually, the surface complexation reaction is composed of the formation of outer-sphere and 

inner-sphere complexes. The formation of outer-sphere complexes is relatively rapid and usually 

treated as an equilibrium process, which is known as steady-state approximation (Selim and 

Amacher, 1996). The formation of inner-sphere complexes is relatively slow, and is often 

calculated by a kinetic law. If the formation of outer-sphere and inner-sphere complexes occurs 

sequentially, the overall rate is determined by the formation of the inner-sphere complexes; if the 
 

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formation of outer-sphere and inner-sphere complexes occurs in parallel reactions, the overall 

rate is determined by the formation of the outer-sphere complexes. Because in our model, the 

formation of outer-sphere complex and inner-sphere complex are treated as parallel reactions, all 

surface complexation reactions are treated as an equilibrium process. It is worth noting that the 

time range to attain equilibrium of sorption reactions is from seconds to days while that for 

mineral dissolution is from days to years or even thousands of years (Selim and Amacher, 1996). 

Therefore, considering the simulation period of interest in current study (100 year), it is safe to 

assume that sorption via surface complexation can be modeled as an equilibrium process, while 

mineral dissolution is kinetically controlled. 

4.2 Model Setup 

The basic model setup for most of the simulations presented here is depicted in Figure 4.1. The 

hydrogeological configuration considered is relatively simple. We consider a confined aquifer 

with a uniform vertical thickness of 10 m at a reference depth of 50 m. The simulation domain 

comprises an area of this aquifer 500 m in length and 200 m in width. Water flows from left to 

right with a flow velocity of 10 m per year (transport velocity). Hydrostatic pressure is assumed; 

i.e., the initial fluid pressure is 5 bar. Hydrogeologic properties, as given in Table 4.1, are 

homogeneous, isotropic, and constant over time. Carbon dioxide is assumed to migrate from a 

deeper geological storage site into the aquifer, representing, for example, local leakage through a 

fault zone. Specifics of these leakage conditions at depth are not important in the context of this 

study; only the rate of CO2 leakage and the area over which it arrives at the freshwater resource 

are relevant. We assume in the base model a continuous CO2 leakage rate of 7.5×10-5 kg/s, which 

corresponds to 2.36 tonnes per year, entering from below over an area of 100 m2

 (10 m × 10 m) 

at x = 105 m and y = 0 m. 

With the intrusion of 2.36 tonnes per year of CO2, a small, two-phase zone evolves where water 

and gaseous CO2 coexist (see simulation results in Section 4.5). However, most of the gaseous 

CO2 quickly dissolves into the groundwater. With time, the CO2-charged groundwater flows 

downstream from the intrusion location, with the result that the acidity of the affected 
 

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groundwater increases, along with the potential for mobilizing hazardous inorganic constituents. 

The simplified model setup is represented, in most simulation cases, as a two-dimensional (2D) 

horizontal depth-averaged model. A sensitivity case simulating a fully three-dimensional (3D) 

setup is described in Section 4.9. 

The potential rate of CO2 leakage into an aquifer may vary widely in field situations. We have 

selected the above rate leakage rate of 7.5×10-5 kg/s, roughly based on the amount of CO2 that 

can dissolve into the groundwater without forming a large two-phase zone of gaseous CO2 and 

water. (The chosen leakage rate was based on a simple back-of-the-envelope analysis, using as 

input the volume of water affected in the aquifer at given flow velocity and the CO2 solubility at 

given pressure and temperature conditions.) Excess CO2 (i.e., gaseous CO2 not dissolving into 

water) would likely migrate upwards and, in an unconfined aquifer, would escape into the vadose 

zone without further impacting groundwater quality. In a confined aquifer, which is the situation 

assumed in this study, excess CO2 would accumulate under the confining layer, largely bypassed 

by the migrating groundwater. We have conducted sensitivity studies varying the CO2 leakage 

rate, as described in Sections 4.6.9 and 4.9.3. 

Groundwater flow 

with 10 m per year

200 m

Gaseous CO2 intrusion at a rate of 5x10-5 kg/s

(1.58 tonnes/yr) for 100 year simulation period

500 m

Confined Shallow Aquifer at 50 m Depth

10 m

x

z

y

Groundwater flow 

with 10 m per year

200 m

Gaseous CO2 intrusion at a rate of 5x10-5 kg/s

(1.58 tonnes/yr) for 100 year simulation period

500 m

Confined Shallow Aquifer at 50 m Depth

10 m

x

z

y

Gaseous CO2 intrusion at a rate of 7.5×10-5 

kg/s (2.36 tonnes/yr) for 100 year simulation 

period

105 m

Fig. 4.1. Simplified model setup for initial geochemical model 
 

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Table 4.1. Hydrodynamic parameters 

Porosity 0.3 

Intrinsic permeability 1×10-12 m2 (hydraulic conductivity of ≈ 0.86 m/day) 

Relative permeability 

(van Genuchten-Mualem model) 

2 0.457 1/ 0.457 * * 1 1 rl kS S

⎧ ⎫ ⎪ ⎛ ⎞ ⎪ = −− ⎡ ⎤ ⎨ ⎜ ⎟ ⎬ ⎣ ⎦ ⎩ ⎭ ⎪ ⎝ ⎠ ⎪

 

with * ( 0.3) /(1.0 0.3) l S S =− − 

where Sl is the liquid saturation degree 

Gas relative permeability ( ) ( ) 2 ˆ ˆ 2 1 1 rg k SS =− − with ( ) ( ) ˆ 0.3 / 1 0.3 0.05 l S S = − −−

Capillary pressure function 1 0.457 1/ 0.457 *

5

1 1

5.1 10

P S cap

− −

−

⎛ ⎞ =− − ⎡ ⎤ ⎜ ⎟ ⎣ ⎦ × ⎝ ⎠ 

with * / 0.999 l S S = and subject to restriction 7 10 0 −≤ ≤ Pcap

Molecular diffusion coefficient 10-9 m2/s 

Due to its simplicity, the hydrogeological situation assumed in this study does not account for 

effects that may affect the system behavior in field situations. These effects include, for example, 

the impact of internal heterogeneity, which could lead to fast preferential transport in the aquifer, 

or the impact of vertical anisotropy in the aquifer’s permeability (e.g., due to bedding), which 

could affect the vertical spreading of the CO2 plume. We chose an idealized hydrogeological 

situation in this simulation study to bring out more clearly the geochemical changes in response 

to intrusion of CO2 and to more easily interpret related sensitivities; in other words, we focused 

on a very complex geochemical system rather than a very complex hydrogeological system. It is 

understood that our simulations are idealized representations that can only serve as a guide to 

actual field behavior, which can vary strongly from place to place. As discussed in Section 6, we 

recommend conducting site-specific model predictions in future studies—ideally combined with 

adequately designed field experiments—where the local hydrogeological conditions would be 

represented in great detail. 
 

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In the base model, the background CO2 partial pressure was set to 0.002 bar (which corresponds 

to a Log P(CO2) value of -2.7). This is about one order of magnitude higher than CO2 levels in 

the atmosphere, consistent with the fact that most groundwaters have elevated P(CO2) levels due 

to the decay of organic matter. The selected background concentration of 0.002 bar falls into the 

range of values calculated from the database analysis described in Section 3.4.3 (Figure 3.17). 

Appelo and Postma (1994) report partial pressures of CO2 dissolved in natural groundwaters 

ranging from 0.0002 bar to 0.01 bar, depending on the host-rock mineralogy. Background partial 

pressure of CO2 in the groundwater may be important in the context of this study, because the 

higher initial P(CO2) values would correlate to lower initial pH in the water, and thus the pH 

change would be less than expected with intrusion of CO2. The initial CO2 partial pressure 

furthermore impacts the initial distribution of Pb, Cd, and As in the aqueous phase or as adsorbed 

species. See Section 4.6.6 for a sensitivity study with respect to initial CO2 concentration. 

Note that the base model assumes reducing conditions with a redox potential of about -0.23 at 

pH around 7.6, defined by the coexistence of pyrite and goethite. The thermodynamic analysis of 

potable groundwater reported in Section 3.4 has demonstrated that the majority of groundwater 

samples represent waters from reducing environments. To evaluate the implications of oxidizing 

conditions, we conducted one simulation study with a redox potential of approximately +0.37 V 

(Section 4.8). 

In this simulation study, we focus on the behavior of Pb and As as the hazardous constituents 

that may be mobilized by the intrusion of CO2. Of the hazardous constituents studied in this 

report, the concentration of Ba, Cd, Sb, and Zn are also affected by the acidic conditions after 

CO2 leakage (Section 3.5). Pb and As have been selected here because these two are most likely 

to exceed MCLs in potable waters as a result of CO2 intrusion (see Section 3.5.) 

The chemical reactions considered in this modeling study include aqueous complexation, mineral 

dissolution/precipitation, and adsorption/desorption via surface complexation, the latter 

considering goethite, illite, kaolinite, and smectite as principal adsorbents. Ion exchange is not 

explicitly considered, since earlier analysis suggested a less significant effect on the chemical 

evolution of As and Pb. Solid solutions are not explicitly considered, because the 
 

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TOUGHREACT simulator offers currently only a very simplified solid-solution model. 

Approximations are made in the current modeling work to offset the omission of an explicit solid 

solution model. As pointed out in Section 3.5, corrections were made to the solubility products of 

arsenopyrite and gudmundite to reflect the assumption that both As and Sb are likely to occur in 

solid solution in pyrite as FeAsS and FeSbS components. The magnitude of each correction was 

based on the calculated maximum SI frequency determined through the analysis of the more than 

38,000 NWIS groundwaters given in Section 3.4.5.2. 

4.3 Model Sequence and Sensitivity Cases 

All simulations were performed using a two-step procedure. First, equilibrium runs were 

conducted with a specified mineralogical composition to establish the initial (steady state) 

chemical composition of the groundwater at equilibrium condition with minerals and adsorbed 

matter before the intrusion of CO2 (see, for example, results described in Section 4.4). Second, 

reactive transport simulations were conducted for the hydrogeological environment shown in 

Figure 4.1, with the initial aqueous chemical composition obtained from the equilibrium run. 

CO2 intrusion starts at time zero and continues for the entire simulation period of 100 years. In 

other words, it is assumed that the CO2 leakage remains undetected and that no remedial 

measures are undertaken. 

Various geochemical sensitivity cases are evaluated to (1) account for the wide range of 

conditions found in domestic potable aquifers and their host rocks, and (2) to encompass the 

uncertainty associated with geochemical processes and model parameters. In addition, we have 

varied certain hydrogeological aquifer conditions, such as the depth of the freshwater resource, 

the groundwater flow rate and CO2 intrusion rates. We have also evaluated the fidelity of the 2D 

model by comparing the results to those of the 3D model. 

Most of the sensitivity cases described in this report assume the basic mineralogy of a Coastal 

Plain Sandstone aquifer, as described in Section 3.6.3.1. This mineralogy is representative of an 

impure sandstone—with a composition consisting of mostly quartz plus some feldspar, lithic 
 

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fragments, and clays—expected to represent one of the more vulnerable aquifers, because the pH 

buffering capacity to CO2 intrusion is limited. We describe in Section 4.4 the initial geochemical 

conditions derived for this type of aquifer and discuss how these conditions compare with the 

measured concentrations of major constituents in potable groundwater described in Section 3.3.2. 

We then provide the simulation results for CO2 intrusion into a Coastal Plain Sandstone, starting 

with the base model (Section 4.5) and followed by various sensitivity cases (Section 4.6). 

Table 4.2. Summary of sensitivity cases 

Base Model Coastal Plain Sandstone Mineralogy Section 4.5 

Sensitivity to Adsorption Section 4.6.1 

Sensitivity to Kinetic Rate Parameters Section 4.6.2 

Sensitivity to PbS(aq) Association Constant Section 4.6.3 

Sensitivity to Mineral Solubility Section 4.6.4 

Geochemical 

Processes and 

Parameters 

Sensitivity to Initial Mineral Volume Fraction Section 4.6.5 

Sensitivity to Background CO2 Concentration Section 4.6.6 

Sensitivity to Depth of Aquifer Section 4.6.7 

Sensitivity to Groundwater Flow Rate Section 4.6.8 

Model Setup, 

Initial and 

Boundary 

Conditions Sensitivity to CO2 Leakage Rate Section 4.6.9 

Other St. Peter Sandstone Mineralogy Section 4.7.1 

Mineralogies Dolomitic Limestone Mineralogy Section 4.7.2 

Oxidizing 

Conditions 

Coastal Plain Sandstone Mineralogy Section 4.8 

Dimensionality 

of Model 

3D Model Section 4.9 

Returning to the base model setup, we report (in Section 4.7) simulations using other 

mineralogical compositions. These include two end-member cases: (a) one having the 

mineralogical characteristics of St Peter Sandstone, which is an almost pure arenite and expected 

to be most vulnerable to CO2 intrusion, and (b) another having the mineralogy of a Dolomitic 

Limestone aquifer, where the pH is well buffered and expected to be less vulnerable to CO2

intrusion. A modified mineralogy representing a Coastal Plain Sandstone in an oxidizing 

environment is used in Section 4.8. All specified mineral compositions contain trace amounts of 
 

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minerals containing hazardous metals or As as essential components, as detailed in Section 

3.6.2.2. A complete list of all sensitivity cases is given below. Some other relevant factors and 

their potential importance are qualitatively discussed in Section 4.10. 

4.4 Initial Geochemical Conditions for Base Model with Coastal 

Plain Sandstone 

The base model simulates the groundwater disturbance by CO2 intrusion into a Coastal Plain 

Sandstone aquifer, as described in Section 3.6.3.1. The base model setup, introduced in Section 

4.2, assumes a two-dimensional depth-averaged model domain with homogeneous properties. 

Gaseous CO2 intrudes from the bottom into the shallow, confined aquifer groundwater with 

groundwater flowing at 10 m per year. Reducing conditions are assumed. 

The basic mineralogy of Coastal Plain Sandstone aquifer, including the hazardous-constituentbearing minerals, is listed in Table 4.3. With respect to minerals containing hazardous 

substances, we consider galena (Pb), arsenopyrite solid solution (As), barite (Ba), greenockite 

(Cd), cinnabar (Hg), gudmundite solid solution (Sb), tiemannite (Se), uraninite (UO2

+2), and 

sphalerite (Zn). However, in our current simulation study, we focus only on the evolution of Pb 

and As. The other listed hazardous constituents are included in the reactive transport simulations, 

but are not further reported here, because either their MCLs are not exceeded in reducing 

groundwaters, or they are believed to be insensitive to CO2 intrusion (Section 3.5). The selection 

of these minerals, as well as their solubilities, is based on the thermodynamic evaluation 

conducted in Section 3.4.5 and is consistent with the equilibrium calculations conducted in 

Section 3.5. Notice the relatively small volume fraction of the hazardous-constituent-bearing 

minerals in Table 4.3, which is a realistic representation based on the review of trace element 

concentrations in soils and sediments given in Section 3.6.2.2. 

Table 4.3 also gives the selected parameters for the rate law used for kinetically controlled 

mineral dissolution and precipiation. TOUGHREACT (Xu et al., 2006) uses a general form of 
 

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rate expression, which is based on transition state theory (TST) (Lasaga et al., 1994; Steefel and 

Lasaga, 1994): 

r kA Q K 1 / ( ) η θ

= − ⎡ ⎤ ⎣ ⎦ (4.1) 

where r is the kinetic rate (positive values indicate dissolution, negative values indicate 

precipitation), k is the rate constant (moles per unit mineral surface area and unit time) which is 

temperature dependent, A is the specific reactive surface area per kg H2O, K is the equilibrium 

constant for the mineral–water reaction written for the destruction of one mole of mineral, and Q 

is the reaction quotient. The parameters θ and η must be determined by experiments, but are 

commonly set equal to unity when experimental quantification is unavailable. The kinetics rate 

constant can be summed from three mechanisms (Lasaga et al., 1994): 

25 25

25

11 11

exp exp 198.15 198.15

1 1

exp 198.15

H

OH

nu H

nu H a a n

H

OH

OH a n

H

E E kk k

RT RT

E k

R T

α

α

⎡ ⎤⎡ ⎤ − − ⎛⎞ ⎛⎞ = −+ − ⎢ ⎥⎢ ⎥ ⎜⎟ ⎜⎟ ⎣ ⎦⎣ ⎦ ⎝⎠ ⎝⎠

⎡ ⎤ − ⎛ ⎞ + − ⎢ ⎥ ⎜ ⎟ ⎣ ⎦ ⎝ ⎠

 (4.2) 

where subscripts nu, H, and OH indicate neutral, acid, and base mechanisms, respectively, E is 

the activation energy, k25 is the rate constant at 25 °C, R is gas constant, T is absolute 

temperature, a is the activity of the species, and n is a power term (constant). Notice that 

parameters θ and η (see Eq. (4.2)) are assumed to be the same for each mechanism. 

TOUGHREACT also provides a general form of species-dependent rate constant, which is 

detailed in Xu et al. (2006). 

Prior to modeling the reactive transport processes related to the intrusion of CO2 (with results 

reported in Section 4.5), an equilibrium run is performed to establish the initial conditions for the 

reactive transport model. We discuss results from this equilibrium run below, to evaluate 

consistency between the simulated initial state of the aqueous phase composition and the 

concentration ranges revealed by the evaluation of groundwater from the NWIS database, as 

described in Section 3.3. A reasonable agreement would demonstrate that our reactive transport 
 

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model, along with its input geochemical properties and thermodynamic data, has been adequately 

defined. Results are given in Tables 4.4 and 4.5, listing the initial chemical compositions with 

respect to major and trace constituents. Additional information is provided in Tables 4.6 and 4.7, 

giving the aqueous speciation of Pb and As for comparison with Table 3.15. 

Table 4.3. Mineral volume fractions, possible secondary minerals, and kinetic properties for a Coastal Plain 

Sandstone. Kinetic parameters from Xu et al. (2006). 

Parameters for Kinetic Rate Law 

Neutral Mechanism Acid Mechanism Base Mechanism 

Mineral Volume 

Fraction 

of solid 

 

A 

(cm2

/g) 

k25 

(mol/m2

/s) 

Ea

(KJ 

/mol) 

k25 Ea n(H+

) k25 Ea n(H+) 

Primary: 

Quartz 0.77 9.8 1.02×10-14 87.7 

K-feldspar 0.0618 9.8 3.89×10-13 38 8.71×10-11 51.7 0.5 6.31×10-12 94.1 -0.823 

Oligoclase 0.06 9.8 1.45×10-12 69.8 2.14×10-10 65 0.457 

Kaolinite 0.017 1.95×105 6.92×10-14 22.2 4.89×10-12 65.9 0.777 8.91×10-18 17.9 -0.472 

Smectite-Ca 0.0136 5.64×105 1.66×10-13 35 1.05×10-11 23.6 0.34 3.02×10-17 58.9 -0.4 

Illite 0.0285 6.68×105 1.66×10-13 35 1.05×10-11 23.6 0.34 3.02×10-17 58.9 -0.4 

Chlorite 0.0106 9.8 3.02×10-13 88 7.76×10-12 88 0.5 

Kerogen-os 2.67×10-3 9.8 3.02×10-13 88 7.76×10-12 88 0.5 

Pyrite 0.00512 12.9 2.52×10-12 62.76 2.34×10-7 43.54 1 

Calcite 0.0151 Assumed at equilibrium 

Goethite 0.00601 1.47×105 2.52×10-12 62.76 2.34×10-7 43.54 1 

Apatite 2.2×10-3 12.9 2.34×10-7 43.54 1 

Barite 4.89×10-4 12.9 2.34×10-7 43.54 1 

Arsenopyrite 9.05×10-6 12.9 2.34×10-7 43.54 1 

Greenockite 3.03×10-6 12.9 2.34×10-7 43.54 1 

Cinnabar 1.46×10-8 12.9 2.34×10-7 43.54 1 

Galena 7.84×10-6 12.9 2.34×10-7 43.54 1 

Gudmundite 3.88×10-7 12.9 2.34×10-7 43.54 1 

Tiemannite 1.22×10-7 12.9 2.34×10-7 43.54 1 

Uraninite 8.005×10-7 12.9 2.34×10-7 43.54 1 

Sphalerite 9.4 ×10-5 12.9 2.34×10-7 43.54 1 

Secondary: 

Dolomite 12.9 2.52×10-12 62.76 2.34×10-7 43.54 1 

Magnesite 9.8 4.57×10-10 23.5 4.17×10-7 14.4 1 

Ankerite 9.8 1.26×10-9 62.76 6.46×10-4 36.1 0.5 

Dawsonite 9.8 1.26×10-9 62.76 6.46×10-4 36.1 0.5 

Smectite-Na 151.6 1.66×10-13 35 1.04×10-11 23.6 0.34 3.02×10-17 58.9 -0.4 

Pyromorphite 12.9 2.34×10-7 43.54 1 

Ferroselite 12.9 2.34×10-7 43.54 1 

Clausthalite 12.9 2.34×10-7 43.54 1 
 

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Table 4.4 shows the initial total aqueous concentration of major constituents, plus some other 

geochemical properties, obtained from the initial equilibrium run. These are compared with the 

respective values obtained at maximum frequency in histograms of the major constituents in 

more than 38,500 potable groundwaters evaluated in the NWIS database analysis, as given in 

Table 3.7. The water composition calculated in the initial equilibrium run with Coastal Plain 

Sandstone is characterized by slightly higher Na and K, slightly lower Ca and Fe, and fairly 

similar concentrations of SiO2, SO4

-2, and Cl-

. The observed differences are quite moderate, 

considering the fact that the reported peak histogram values in Table 3.7 typify very broad 

concentration distributions, characterized by the range and variety of geochemical conditions 

encountered. The water compositions show slightly different values for the partial pressure of 

CO2, pH, and Eh. The difference with respect to Ca is in part caused by the fact that the water 

composition calculated for the Coastal Plain Sandstone had lower alkalinity and higher pH, 

assuming the water is in equilibrium with calcite—whereas the measured concentrations at peak 

value are slightly oversaturated with respect to calcite (based on a EQ3 run using the analytical 

peak concentrations). There are, however, considerable differences with respect to Mg and Al 

(respectively 40 times and 2,000 times smaller in the simulated concentration). These 

discrepancies are caused by some limitations in the current geochemical model, which we 

discuss briefly in the following paragraphs. 

Normalized saturation indices of a suite of minerals commonly encountered in sedimentary 

environments are listed in Table 4.4a for initial secular equilibrium conditions generated for the 

Coastal Plain Sandstone and for modal chemical analyses of NWIS potable water composition. 

The saturation indices are normalized to unit silica stoichiometry for silicates and unit carbonate 

stoichiometry for carbonates. This normalization procedure affords a better basis for 

intercomparison of mineral saturations, because the saturation index is an extensive parameter 

that can vary depending on how the chemical formula of a mineral is written. Both the Coastal 

Plain Sandstone aquifer initial condition and the potable water compositions are concordant in 

displaying significant supersaturation with respect to quartz, the former due to kinetic constraints 

and the latter due to specification of saturation with respect to opal-CT. Both also display 

significant undersaturation with respect to dolomite, consistent with field observations indicating 
 

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that de-dolomitization can occur in shallow freshwater aquifers. Otherwise, the saturation indices 

of most silicates and carbonates indicate a relatively close approach to saturation, and fall within 

the range of uncertainty expected from the level of accuracy in log K values of mineral solubility 

products. 

Table 4.4. Initial total aqueous concentration of major constituents obtained by initial equilibrium run for Coastal 

Plain Sandstone and analytical concentration at the maximum frequency in the histogram of 38,500 

potable groundwaters and saturation indices of some major minerals 

Species Total Aqueous 

Concentration (mol/L) 

Total Aqueous 

Concentration (mg/L) 

Modal values of Analytical 

Concentrations at (mg/L) 

Eh -0.215 (volt) -0.18 (volt) 

pH 7.61 7.3 

Ca 9.00E-04 36.0 63.1 

Mg 2.17E-05 0.52 20 

Na 1.97E-03 45.36 12.6 

K 2.68E-04 10.47 1.6 

Fe 5.63E-06 0.31 1.3 

Si 9.31E-04 55.9 75 

TIC as HCO3

-

 3.33E-03 203.1 316 

SO4

-2 1.95E-04 18.7 20 

Al 4.19E-11 1.1E-06 0.002 

Cl 2.07E-04 7.3 15.8 

P(CO2) (log bar) -2.3 -2.3 -1.86 

ionic strength (mol/L) 0.0051 0.0078 

Noteworthy in both cases is the relatively close approach to saturation with respect to K-feldspar, 

in contrast to significant undersaturation with respect to low albite. However, the degree of 

supersaturation of Ca- and Na-smectites in the potable waters suggests deficiencies in the 

thermodynamic data for these minerals. Furthermore, the very large level of supersaturation 

predicted for chlorite (Mg2.5Fe2.5Al2Si3O10(OH)8) in the potable water suggests that either the 

computed solubility product for this mineral was inadequate, or that some other Mg-bearing 

mineral is controlling the Mg activity at metastably elevated levels in potable groundwaters. In 

this regard, note that talc (Mg3Si4O10(OH)2) is close to saturation. It is geochemically 
 

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unreasonable to expect talc to occur in most shallow arenaceous aquifers, but the presence of an 

otherwise unrecognized poorly crystallized magnesian-rich phyllosilicate metastably saturating 

such aquifers, especially in aquifers undergoing dedolomitization, cannot be discounted. 

The observed apparent chlorite supersaturation may also be attributed to the higher Al 

concentration reported in potable groundwaters than predicted from thermodynamic calculations. 

A partial explanation for this phenomenon is given in Section 3.3.2, where it is noted that the 

precipitation of metastable amorphous hydrated aluminum silicates caused by the dissolution of 

unstable detrital feldspars is not accounted for, a process recognized by Paces (1978) several 

decades ago. Both the kinetics of feldspar alteration and the associated precipitation of 

amorphous hydrated aluminum silicates are poorly understood, and further insight into the 

dissolution/precipitation mechanisms involved is needed before this process can be incorporated 

satisfactorily in geochemical models. However, recognition of this process may also explain the 

apparent chlorite supersaturation noted above. 

The very large discrepancy between the modeled Al concentration in the Coastal Plain Sandstone 

aquifer initial condition and the modal value of analytical concentrations in NWIS groundwaters, 

as shown in Table 4.4 is primarily caused by the model deficiency described above. However, a 

further contribution, also discussed in Section 3.3.2, is the omission from the TOUGHREACT 

database of alumino-silicate complexes described by Tagirov and Schott (2001). These 

complexes can increase the total Al concentration in solution by a factor of ten. In conclusion, 

we may infer that the primary cause for the discrepancies between modeled and observed 

aqueous concentrations in both Mg and Al is the failure to account for secondary precipitation of 

amorphous or cryptocrystalline Mg- and Al-hydroxy-silicates. It is beyond the scope of this 

study to investigate this matter further, particularly when it is recognized that failure to take this 

issue into account has an insubstantial impact on the thermodynamic and kinetic controls 

affecting the concentrations of hazardous inorganic constituents in groundwaters, the primary 

subject of investigation. There are several contributing reasons why the observed Mg and Al 

discrepancies are not relevant in the context of this study, as explained below. 
 

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First, the activities of all of the hazardous constituents under study are determined by the 

solubilities of their mineral hosts, which have been identified as sulfides or selenides, carbonates 

and a silicate. None involve the participation of minerals containing alumina. Second, the pH of 

the aqueous phase is normally buffered primarily by carbonate minerals, especially by calcite. In 

the absence of carbonates, aluminosilicates, especially clays, may play an important role in 

buffering pH, but it is the well-defined amphoteric equilibrium between aqueous hydroxyalumina species that determines buffering capacity, rather than the solubility product of 

aluminosilicates. It should be noted that the solubility products of most aluminosilicate minerals 

incorporated in both EQ3/6 and TOUGHREACT thermodynamic databases are derived from 

calorimetric measurements, rather than from direct measurement of their solubilities. In 

geochemical modeling of aqueous systems in which aluminosilcates play a part, the system is 

specified by Gibbs phase rule, and the concentration of Al in solution, defined in relation to the 

basis species, Al+3 in EQ3/6, or AlO2

-

 in TOUGHREACT, is a dependent variable that is 

generally ignored, unless, as in the present study, an attempt is made to validate model 

predictions against field observation. Finally, Eh is specified primarily through equilibria 

between pyrite and goethite at a given pH. Dissolved Al does not play a role in defining the 

redox state of the system, yet it is the redox state that is generally critical in specifying the 

activities of the studied hazardous constituents when in equilibrium with their mineral hosts. In 

summary, aluminum speciation in solution does not play a direct role in modifying the 

thermodynamics or kinetics of the activities of hazardous constituents in solution, and its 

amphoteric behavior in buffering pH is also not at issue. 

Table 4.5 lists the initial aqueous concentration of all hazardous inorganic constituents in 

groundwater. The predicted total aqueous Pb concentration is 1.3×10-9 mol/L (2.68×10-4 mg/L), 

which corresponds to a local maximum in the histogram of measured Pb concentrations from the 

NWIS database for potable groundwaters (see Figure 3.6a in Section 3.3.3). The predicted total 

aqueous As concentration is 4.42×10-8 mol/L (3.31×10-3 mg/L), which falls close to the 

maximum frequency of As concentration observed in NWIS analyses (see Figure 3.6b in Section 

3.3.3). Similarly good agreement between the simulated initial concentrations and the range of 

values reported from the database is also observed for the other hazardous inorganic constituents. 
 

212 of 352

Notice, furthermore, that the initial aqueous concentrations are fully consistent with the trace 

metal concentrations displayed in Figure 3.51 (at the selected log P(CO2) level of -2.3), where 

equilibrium concentrations as a function of CO2 partial pressure have been determined from 

EQ3/6 calculations. The above comparisons, for both major and trace elements, demonstrate that 

the geochemical conditions in the base model at initial equilibrium state are reasonably 

consistent with the average conditions observed in a large number of United States aquifers. 

Table 4.4a. Comparison of normalized saturation indices of some major minerals from the initial equilibrium 

run for Coastal Plain Sandstone with those calculated for potable groundwater with analytical 

concentrations at the maximum frequency in 38,500 potable groundwaters 

Normalized Saturation Index Mineral 1 

Initial condition, 

Coastal Plain 

Sandstone 

At maximum 

frequency in 

potable NWIS 

groundwaters 

Comments 

Primary:

Quartz +0.71 +0.84 

Opal-CT - 0.00 Saturation specified 

K-feldspar -0.11 +0.16 

Albite_lo

w 

-0.90 -0.48 

Kaolinite -1.23 +0.25 

Talc - +0.24 

SmectiteCa -0.02 +0.75 

Smectitelow-FeMg - 0.00 Saturation specified in NWIS groundwater 

Illite +0.02 +0.03 

Chlorite +0.00 +2.21 

Pyrite 0.00 0.00 Saturation specified 

Calcite 0.00 +0.09 Saturation specified in simulation 

Goethite 0.00 0.00 Saturation specified 

Secondary:

Dolomite -1.77 -1.07 

Magnesite -1.91 -4.19 

Ankerite -0.35 +0.08 

Siderite -0.58 -0.05 

Nasmectite -0.11 +0.61 

1

Normalized to unit SiO2 for silicates, and unit CO3 for carbonates 
 

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Table 4.5. Initial chemical composition of trace elements of groundwater from the TOUGHREACT equilibrium 

simulation in comparison 

Species Total Aqueous 

Concentration (mol/L) 

Total Aqueous 

Concentration (mg/L) 

Pb 1.30E-09 2.69E-04 

As 4.42E-08 3.31E-03 

Ba 1.12E-06 1.53E-01 

Cd 1.76E-09 1.98E-04 

Hg 2.53E-09 5.08E-04 

Sb 3.57E-10 4.34E-05 

Se 1.69E-08 1.33E-03 

U 4.48E-09 1.07E-03 

Zn 3.40E-07 2.22E-02 

With respect to aqueous speciation, the concentrations of Pb aqueous species listed in Table 4.6 

are consistent with the findings discussed in Section 3.5.4 and the values presented in Table 3.15. 

At the selected CO2 partial pressure of –2.3 (in log10), the most important Pb aqueous complex 

is PbS(aq), which accounts for about 85% of the total aqueous Pb. PbSe(aq) accounts for about 

14% and PbCO3(aq) for about 1%. Table 4.7, which shows aqueous As speciation, indicates that 

arsenite (As III) is the dominant species at the selected initial conditions and mainly exists as a 

complex of H3AsO3(aq), while arsenate (As V) can be neglected. These results are consistent 

with those derived from EQ3/6 calculations (Table 3.16). 
 

214 of 352

Table 4.6. Concentrations of aqueous species of Pb 

Species Concentration (mol/L) Percentage 

PbCO3(aq) 1.50E-11 1.15% 

Pb(HS)2(aq) 7.93E-18 0.00% 

Pb (HS)3

-

 9.63E-26 0.00% 

PbCl+ 8.67E-16 0.00% 

PbCl2(aq) 5.50E-19 0.00% 

PbCl3

-

 5.50E-23 0.00% 

PbCl4

-2 8.57E-27 0.00% 

PbOH+ 1.75E-13 0.01% 

Pb(OH)2(aq) 2.07E-15 0.00% 

Pb (OH)3

-

 9.25E-19 0.00% 

Pb (CO3)2

-2 1.15E-14 0.00% 

PbO(aq) 2.58E-15 0.00% 

PbHCO3

+

 3.59E-13 0.03% 

Pb+2 2.06E-13 0.02% 

Pb(HSe)2(aq) 6.56E-24 0.00% 

Pb(HSe)3- 4.76E-38 0.00% 

PbS(aq) 1.11E-09 84.80% 

PbSe(aq) 1.83E-10 13.99% 

Total Pb 1.30E-09 100% 

Table 4.7. Speciation of aqueous species of As 

Species Concentration (mol/L) Percentage 

H3AsO3(aq) 4.28E-08 96.79% 

H2AsO3

-

 1.41E-09 3.19% 

H2AsO4

-

 1.77E-13 0.00% 

H3AsO4(aq) 6.89E-19 0.00% 

HAsO4

-2 1.63E-12 0.00% 

H3As3S6(aq) 7.84E-46 0.00% 

H2As3S6

-

 8.36E-42 0.00% 

HAs3S6

-2 4.20E-42 0.00% 

H3As3Se6(aq) 3.93E-77 0.00% 

H2As3Se6

-

 1.39E-73 0.00% 

HAs3Se6

-2 5.75E-75 0.00% 

HAsS2(aq) 6.51E-20 0.00% 

AsSe(OH)(HSe)- 1.63E-19 0.00% 

AsS(OH)(HS)- 8.83E-12 0.02% 

Total As 4.42E-08 100% 
 

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4.5 Results for CO2 Intrusion into Coastal Plain Sandstone 

4.5.1 Prediction of CO2 Plume Evolution and Changes in Acidity 

In our simulation scenario, CO2 gas enters the aquifer from the bottom at x = 105 m and y = 0 m, 

over a horizontal area of 10 m × 10 m. The dissolution of CO2 into water leads to the formation 

of various aqueous carbonate complexes and therefore the total amount of dissolved CO2 is given 

by the concentration of total inorganic carbon (TIC). The intrusion of CO2 leads to an increase of 

TIC (Figure 4.2 left) and a corresponding decrease of pH (Figure 4.2 right). Note that the 

variation in concentration of TIC is the combined effect of CO2 intrusion, dissolution of CO2 in 

water, and chemical reactions (mainly as calcite dissolution in the current simulation). While 

initially all CO2 dissolves almost instantaneously into the groundwater, a gas phase evolves after 

around 3 years close to the intrusion area (Figure 4.3). Both water and gaseous CO2 are present 

in this area, forming a small two-phase zone that remains present until the end of the simulation 

period at 100 years (Figure 4.4). The CO2 gas dissolves into the groundwater and is transported 

downstream (Figure 4.5). The plume of dissolved CO2 (total carbonate) migrates primarily along 

the groundwater flow direction, while lateral spreading of dissolved CO2 is minor, penetrating 

only up to 20 m in the y-axis direction (direction perpendicular to groundwater flow). The spatial 

distribution of pH shown in Figure 4.6 closely follows the distribution of TIC. The two-phase 

zone has the highest partial pressure of CO2 and thus largest TIC concentration, and therefore, 

the pH in the two-phase zone is the lowest. 

Notice in this context that the TOUGHREACT transport model has been set up without 

consideration of hydrodynamic (longitudinal or transverse) dispersion, which would induce more 

spreading. The process of hydrodynamic dispersion is often represented by a Fickian diffusion 

analogue (convection–dispersion equation) that has some flaws and limitations (Xu and Pruess, 

2001). A Fickian model for dispersion is not used in TOUGHREACT. Instead, hydrodynamic 

dispersion is introduced through spatial resolution. The discretization used for the transport 

simulations employs a grid size of 10 m in the horizontal directions, implying that both the 

longitudinal and transverse dispersivities are on the order of about 10 m. In our study, modeling 
 

216 of 352

without explicit consideration of hydrodynamic dispersion means that less spreading and dilution 

of the dissolved CO2 plume occurs, thus maximizing (at least locally) the impacts of CO2

intrusion on the geochemistry of the system. If hydrodynamic dispersion was significantly larger 

than assumed here, the area affected by dissolved CO2 would increase, but the maximum 

concentrations would be reduced due to enhanced dilution of the plume. 

0 20 40 60 80 100

Time (year)

0

0.04

0.08

0.12

0.16

TIC (mol/L)

0 20 40 60 80 100

Time (year)

5.5

6

6.5

7

7.5

8

pH

 

Fig. 4.2. Time evolution of total inorganic carbon (TIC) (left) and pH (right) at CO2 intrusion location 

0 20 40 60 80 100

Time (year)

0

0.02

0.04

0.06

0.08

0.1

Gas saturation degree

Fig. 4.3. Time evolution of gas saturation degree at CO2 intrusion location 
 

217 of 352

X

Y

100 200 300 400

50

100

150

200

SG

0.075

0.070

0.065

0.060

0.055

0.050

0.045

0.040

0.035

0.030

0.025

0.020

0.015

0.010

0.005

Fig. 4.4. Spatial distribution of saturation degree of CO2 gas after 100 years of intrusion 

X

Y

100 200 300 400

50

100

150

200

TIC

0.15

0.14

0.13

0.12

0.11

0.10

0.09

0.08

0.07

0.06

0.05

0.04

0.03

0.02

0.01

Fig. 4.5. Spatial distribution of total inorganic carbon (TIC) (mol/L) after 100 years of intrusion 
 

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X

Y

100 200 300 400

50

100

150

200

pH

7.49

7.37

7.24

7.12

7.00

6.88

6.75

6.63

6.51

6.39

6.26

6.14

6.02

5.90

5.77

Fig. 4.6. Spatial distribution of pH after 100 years of intrusion 

Figure 4.7 shows profiles of TIC (left) and pH (right) at four times after the start of intrusion of 

CO2, i.e., at 5, 20, 50, and 100 years. The profiles are taken along the direction of flow (x-axis) at 

y = 0 m. The plume of dissolved CO2 migrates with the groundwater flow over time, extending 

further and further downstream until a steady state is reached within the model domain. The 

evolution of pH closely follows the evolution of TIC, with pH decreasing as the concentration of 

TIC rises. After about 50 years, the region of decreased pH extends from the CO2 intrusion point 

downstream to the boundary of the model domain. Notice that the lowest pH is around 5.87 

outside of the two-phase zone, which is higher than the expected pH in a system without any 

buffering capacity. (For a nonbuffered system, a pH around 4.5 would be expected). The 

moderation in pH decrease is caused primarily by the dissolution of calcite. Figure 4.8 shows the 

volumetric change in calcite caused by dissolution. Almost all of the calcite initially present near 

the two-phase zone has dissolved after 100 years. This significant dissolution of calcite buffers 

the pH response to the increasing CO2 levels in the water; however, this buffering effect would 

cease locally if CO2 intrusion were to continue after 100 years, since calcite would eventually be 

depleted. 
 

219 of 352

0 100 200 300 400 500

Distance (m)

0.0x100

4.0x10-2

8.0x10-2

1.2x10-1

1.6x10-1

TIC (mol/L)

5 yr

20 yr

50 yr

100 yr

0 100 200 300 400 500

Distance (m)

5.6

6

6.4

6.8

7.2

7.6

8

pH

5 yr

20 yr

50 yr

100 yr

Fig. 4.7. Total inorganic carbon (left) and pH (right) along the x-axis at y = 0 m, for Coastal Plain Sandstone. 

0 100 200 300 400 500

Distance (m)

-100

-80

-60

-40

-20

0

20

Calcite volume fraction change (%)

5 yr

20 yr

50 yr

100 yr

Fig. 4.8. Calcite volume fraction change profiles along x-axis at y = 0 m, for Coastal Plain Sandstone. 

4.5.2 Prediction of Pb and As Concentrations 

The processes that control the changes in aqueous Pb and As concentration in response to CO2

intrusion include desorption/adsorption from the surface of minerals, dissolution/precipitation of 

relevant minerals (galena for Pb and arsenopyrite in solid solution for As), and the 

dissociation/association of aqueous complexes. The resulting concentrations are moreover 

affected by complex interaction between these processes. 
 

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Figure 4.9 shows the contour map of total aqueous Pb concentration after 100 years, which 

resembles closely the spatial distribution of the pH decrease. The highest concentration occurs 

along the y=0 profile. Transport in the longitudinal direction is dominant. Figure 4.10 shows the 

total aqueous Pb concentration profile along the x-axis at y = 0 m for different times. The peak 

value of aqueous Pb is 3.4×10-8 mol/L in the small two-phase zone at the intrusion location after 

5 years, which then decreases to 1.8×10-8 mol/L after 50 years. As time progresses, the peak 

moves incrementally downstream, and after 100 years falls to 1.6×10-8 mol/L. The aqueous Pb 

concentration downstream approaches a stable value around 1.1×10-8 mol/L. This value is 

significantly higher (about one order of magnitude) than the initial concentration, but remains 

below the MCL (maximum contamination level), which is 7.24× 10-8 mol/L (15 ppb). The 

observed Pb concentration profiles reflect a combination of dissolution/precipitation and 

adsorption/desorption effects, as discussed below. 

X

Y

100 200 300 400

50

100

150

200

Pb

1.5E-08

1.4E-08

1.3E-08

1.2E-08

1.1E-08

1.0E-08

9.1E-09

8.2E-09

7.2E-09

6.3E-09

5.4E-09

4.4E-09

3.5E-09

2.5E-09

1.6E-09

Fig. 4.9. Spatial distribution of total aqueous Pb after 100 years of intrusion 
 

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0 100 200 300 400 500

Distance (m)

0x100

2x10-8

4x10-8

6x10-8

8x10-8

Total aqueous Pb concentration (mol/L)

5 yr

20 yr

50 yr

100 yr

MCL

Fig. 4.10. Total aqueous Pb concentration profile along x-axis at y = 0 m. 

Our results for the base-case model suggest that adsorption and desorption of Pb+2 ion from 

mineral surfaces are more important to the evolution of aqueous Pb concentrations than the 

precipitation and dissolution of galena. Figure 4.11 exemplifies the complexity of these 

processes by illustrating the change in the surface complex concentrations for illite. The surface 

complex concentration is expressed in mol/L solution. Desorption and adsorption constitute, 

respectively, the net effects of decreasing and increasing the concentration for all relevant 

surface complexes. Illite is the most important adsorbent of Pb. Ills_OPb+ is a surface complex of 

Pb on illite (Appendix F), which represents the mass of Pb on the strong site at the surface of 

illite, while Illw_OPb+

 represents the mass of Pb on the weak site at the surface of illite. The 

increase of Ills_OPb+ (Figure 4.11 left) compared to the initial levels indicates that aqueous Pb is 

adsorbed on the strong site, whereas the decrease of Illw_OPb+

 (Figure 4.11 right) indicates that 

aqueous Pb is released from the weak site on illite. Another important surface complex that 

adsorbs Pb is smes

_OPb+, which represents the mass of Pb on the strong site at the surface of 

smectite. 

Figure 4.12 shows the relative change of total Pb concentration on adsorption sites (which is a 

summation of the concentration of all Pb surface complexes), calculated as: 

t ini

ini

S S

S

− (4.3) 
 

222 of 352

where St is the total Pb concentration on adsorption sites at a given time t, and Sini is the initial 

total Pb concentration on adsorption sites (both given in mol/L solution). The figure indicates 

that significant desorption of Pb occurs in the two-phase zone (depleting the initially sorbed Pb 

by about 60% after 100 years), thereby increasing the aqueous Pb concentration. Less effective 

desorption occurs further downstream, although the Ph there is similarly small. The overall effect 

of desorption is smaller because the aqueous Pb mobilized in the two-phase zone migrates 

downstream, inhibiting further desorption. 

0 100 200 300 400 500

Distance (m)

0.0x100

4.0x10-8

8.0x10-8

1.2x10-7

1.6x10-7

2.0x10-7

ills_OPb+ (mol/L)

5 yr

20 yr

50 yr

100 yr

0 100 200 300 400 500

Distance (m)

10-6

2x10-6

3x10-6

4x10-6

5x10-6

6x10-6

illw_OPb+ (mol/L)

5 yr

20 yr

50 yr

100 yr

Fig. 4.11. Surface complex of Pb on the strong (left) and weak site (right) of illite along x-axis at y = 0 m for 

Coastal Plain Sandstone. 

0 100 200 300 400 500

Distance (m)

-80

-60

-40

-20

0

20

Total Pb sorption sites change (%)

100 yr

Fig. 4.12. Relative change of total Pb surface complexes concentration along the x-axis at y = 0 m for Coastal 

Plain Sandstone. 
 

223 of 352

Section 3.5 illustrated how the solubility of galena increases with the partial pressure of CO2 and 

presented the expected equilibrium concentrations of Pb and other inorganic hazardous 

constituents at various P(CO2) levels. It is important to point out, however, that these 

calculations (in Section 3.5.3) represent an equilibrium state, which does not take into account 

transient changes in adsorption/desorption and precipitation/dissolution. The ingress of CO2

immediately disturbs both adsorption and solubility equilibria, but whereas adsorption reactions 

are always assumed to re-equilibrate instantaneously, the dissolution precipitation of a hazardous 

mineral host is kinetically controlled and typically much slower. As we can observe from the 

transient runs conducted here, the relatively sudden release of Pb from adsorption sites increases 

the aqueous Pb concentration so significantly that the solution becomes in fact supersaturated 

with respect to galena (even though the groundwater becomes more acidic, suggesting a higher 

galena solubility), and galena precipitation occurs in the two-phase zone. 

Figure 4.13 exemplifies this process, giving the relative change in the galena volume fraction as 

a function of time. The volume fraction change is calculated as: 

t ini

ini

V V

V

− (4.4) 

where Vt is the volume fraction of mineral at given time t, and Vini is the initial volume fraction 

of mineral. Negative values indicate dissolution, positive values indicate precipitation. Notice 

that galena continues to precipitate in the two-phase zone over the entire simulation period of 

100 years. At later stages, however, galena may start dissolving, because the Pb on adsorption 

sites will have eventually depleted. 

As pointed out in Section 3.5, sulfide and selenide aqueous complexation play a very important 

role in the behavior and evolution of several of the hazardous constituents, most notably Pb, but 

also cadmium, mercury, and zinc. Because very stable neutral complexes form in solution, the 

impact that elevated CO2 levels have on increasing the solubility of galena (PBS), for example, is 

reduced compared to a case where no complexation is considered, due to the competition 

between chalcogenide complexes and carbonate complexes. While not reported here, we have 
 

224 of 352

conducted reactive transport simulations without accounting for sulfide and selenide complexes 

in solution. These resulted in higher overall aqueous Pb concentrations after CO2 intrusion 

compared to the base model, because both desorption and galena dissolution caused Pb 

mobilization in the two-phase zone. 

0 100 200 300 400 500

Distance (m)

-0.1

0

0.1

0.2

0.3

0.4

Galena volume fraction change (%)

5 yr

20 yr

50 yr

100 yr

Fig. 4.13. Galena volume fraction change profiles along x-axis at y = 0 m, for Coastal Plain Sandstone. 

Figures 4.14 and 4.15 (left) show, respectively, the spatial distribution of As concentration at 100 

years and the evolution of As concentration in a profile along the x-axis. The peak concentration 

of As is 1.29 × 10-7 mol/L, with the As concentration stabilizing at 1.19 × 10-7 mol/L 

downstream, slightly less than the MCL (1.33 × 10-7 mol/L or 10 ppb). The increases in As 

concentration are constrained to a narrow area downstream of the CO2 intrusion point. 

As was the case with Pb, the observed As concentrations are significantly affected by 

adsorption/desorption processes. In the base model, illite is the most important adsorbent for 

both Pb and As, but as conditions change, other minerals may become more relevant, depending 

on the thermodynamic solubility, specific adsorption site density, and volume fraction of 

minerals present in the aquifer host rock. The adsorption/desorption of As exhibits even more 

complexity than that of Pb. For example, H2AsO3 (aq) is adsorbed on illite as three types of 

surface complexes: ill _H2AsO3, ill _HAsO3

-

, and ill _AsO3

2-. Desorption or adsorption is the net 

effect of increase or decrease of As concentration on all three surface complexes, as well as 
 

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surface complexes on other minerals. For example, a decrease in ill_HAsO3

-

 (Figure 4.16, left) 

indicates the release of initially adsorbed As into water, whereas an increase in ill _H2AsO3

(Figure 4.16, right) indicates that aqueous As is adsorbed. The relative change of total As 

surface-complex concentration shown in Figure 4.17 (left) indicates that desorption occurs in the 

two-phase zone, thus increasing the aqueous As concentration, whereas adsorption occurs further 

downstream, thus limiting further increases in aqueous As. 

X

Y

100 200 300 400

50

100

150

200

As

1.2E-07

1.2E-07

1.1E-07

1.1E-07

1.0E-07

9.7E-08

9.2E-08

8.7E-08

8.1E-08

7.6E-08

7.1E-08

6.5E-08

6.0E-08

5.5E-08

4.9E-08

Fig. 4.14. Spatial distribution of total aqueous As concentration after 100 years of intrusion 

0 100 200 300 400 500

Distance (m)

4.0x10-8

6.0x10-8

8.0x10-8

10-7

1.2x10-7

1.4x10-7

Total aqueous As concentration (mol/L)

5 yr

20 yr

50 yr

100 yr

MCL

Fig. 4.15. Total aqueous As concentration profiles along the x-axis at y = 0 m, for Coastal Plain Sandstone. 
 

226 of 352

0 100 200 300 400 500

Distance (m)

0

0.01

0.02

0.03

ill_HAsO3

- (mol/L)

5 yr

20 yr

50 yr

100 yr

0 100 200 300 400 500

Distance (m)

0

0.01

0.02

0.03

0.04

ill

_

H2AsO3 (mol/L)

5 yr

20 yr

50 yr

100 yr

Fig. 4.16. Surface complex Ill _HAsO3

-

 (left) and Ill _H2AsO3 (right) profiles along the x-axis at y = 0 m, for 

Coastal Plain Sandstone. 

0 100 200 300 400 500

Distance (m)

-0.04

0

0.04

0.08

0.12

Total As sorption sites change (%)

100 yr

0 100 200 300 400 500

Distance (m)

-0.04

-0.03

-0.02

-0.01

0

Arsenopyrite volume fraction change (%)

5 yr

20 yr

50 yr

100 yr

Fig. 4.17. Relative change of total As surface complexes concentration (left) and arsenopyrite volume fraction 

change (right) along the x-axis at y = 0 m for Coastal Plain Sandstone. 

Figure 4.17 (right) shows the volume fraction change of arsenopyrite in solid solution. In 

contrast to the Pb observations, where the metal-hosting mineral (galena) actually precipitates 

after intrusion of CO2, As is mobilized by arsenopyrite dissolution in the two-phase zone and, to 

a lesser degree, in the downstream region. Thus, in the case of As, the decrease of pH in the twophase zone causes both desorption of As from the mineral surface (Figure 4.17 left) and 

dissolution of arsenopyrite (Figure 4.17 right), generating a concentration increase almost to the 
 

227 of 352

MCL. Further increases of As concentrations downstream of the two-phase zone are limited, 

because the water arriving from upstream already has elevated As concentrations. As a result, the 

dissolution of arsenopyrite becomes less effective, and aqueous As sorbs back to the mineral 

surfaces (Figure 4.17 left). That the dissolution of arsenopyrite is a rather slow process becomes 

evident in Figure 4.17 (right), where the maximum volumetric change of arsenopyrite after 100 

years is only about 0.03% of its initial volume fraction. This suggests that the rather small initial 

volume of arsenopyrite in solid solution can be a long-lasting source of aquifer contamination. 

The differences in the two-phase-zone behavior of Pb and As in response to CO2 intrusion—

desorption combined with galena precipitation in the case of Pb compared to desorption and 

arsenopyrite dissolution in the case of As—point to the complexities involved in reactive 

transport modeling and the interplay between the various processes involved. For example, as 

shown in Figure 3.51 (see Section 3.5.3), the solubility of arsenopyrite increases more 

significantly with the partial pressure of CO2 than the solubility of galena, because sulfide and 

selenide complexation effectively inhibit PbCO3(aq) complexation. This is one of the main 

reasons why the desorption of As does not cause supersaturation with respect to arsenopyrite, 

while the desorption of Pb causes supersaturation with respect to galena. However, variations in 

the kinetic rates or changes in other geochemical parameters could affect the observed trends. 

The following tables show how Pb and As are initially distributed in the aquifer between solid 

and aqueous phases, and how this initial distribution changes during the 100 years of CO2

intrusion. Table 4.8 expresses the mass (mol/L solution) of Pb and As in solid (as an essential 

component in a mineral and adsorbed on mineral surfaces) and aqueous phases in the two-phase 

zone near the CO2 intrusion point, at t = 0 and t = 100 years. Table 4.9 gives the same 

information, but relative to the initial (t = 0) total mass of Pb and As, respectively. 

Initially, most Pb resides in the solid phase: about 99% as an essential component in galena and 

about 1% on adsorption sites, while the relative amount of Pb in the aqueous phase is very low 

(around 2×10-4 %). After 100 years, the total mass of Pb on adsorption sites has decreased by 

about two-thirds as a result of desorption, while Pb as an essential component in galena increases 

marginally as a result of the galena precipitation. In contrast, the mass of Pb dissolved in the 
 

228 of 352

aqueous phase has increased by almost one order of magnitude compared to the initial value. In 

absolute numbers, however, the relative amount of Pb in the aqueous phase is still marginal 

compared to the solid phases. This points out that there is enough Pb in the aquifer rock to 

provide a long-lasting source for aqueous contamination. Total mass of Pb in all phases 

decreases by about 0.3%, indicating that part of the dissolved Pb is transported downstream. 

Arsenic in the solid phase, both on adsorption sites and in arsenopyrite, decreases only 

marginally during the 100 years of CO2 intrusion, while the mass of As dissolved in the aqueous 

phase increases by a factor of about three. Note that adsorbed mass accounts for the majority of 

As in the solid phase (about 98% adsorbed compared to about 2% as an essential component in 

arsenopyrite). 

Table 4.8. Mass distribution (mol/L solution) of Pb and As on solid (as an essential component in minerals or 

adsorbed on mineral surfaces) and liquid phase in two-phase zone at initial (t=0) and final time (t=100 

years) 

Solid Phase 

Species Aqueous Phase 

(mol/L solution) Mineral 

(mol/L solution) 

Surface 

(mol/L solution) 

Total Mass 

(mol/L solution) Time 

Pb 1.30E-09 0.000578 5.62E-06 0.000583 t=0 

Pb 1.41E-08 0.000580 1.97E-06 0.000582 t=100 year 

As 4.42E-08 0.000795 0.0383625 0.039157 t=0 

As 1.29E-07 0.000794 0.0383584 0.039153 t=100 year 

Table 4.9. Mass distribution (%) of Pb and As on solid (as essential an component in minerals or adsorbed on 

mineral surfaces) and liquid phase in two-phase zone at initial (t=0) and final time (t=100 years) 

Solid Phase 

Species Aqueous Phase 

Mineral Surface 

Total Time 

Pb 2.23E-04 99.036 0.964 100 t=0 

Pb 2.41E-03 99.336 0.338 99.68 t=100 year 

As 1.13E-04 2.029 97.971 100 t=0 

As 3.30E-04 2.029 97.960 99.989 t=100 year 
 

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4.6 Sensitivity Analyses 

In this section, we discuss the results of a series of sensitivity analyses with respect to 

geochemical processes, model setup, and initial and boundary conditions. The base model is 

described in Section 4.2, and the modeling results concerning this case are given in Section 4.5. 

These results suggest that adsorption/desorption and mineral dissolution/precipitation act 

together as the most important processes that control the evolution of aqueous Pb and As. 

Previous work on the subject by Wang and Jaffe (2004) did not consider adsorption/desorption. 

Therefore, to explore the role of adsorption/desorption, we conducted a sensitivity analysis as 

described below, evaluating the geochemical effects of CO2 intrusion with or without 

consideration of adsorption/desorption. Further sensitivity analyses were performed on other key 

chemical parameters, including solubility parameters, the volume fraction of minerals containing 

hazardous constituents, and surface complexation constants. Finally, sensitivity analyses were 

performed on the hydrodynamic assumptions of the base model: background CO2 concentration, 

aquifer depth, groundwater flow rate, and CO2 intrusion rate. 

4.6.1 Sensitivity to Adsorption 

4.6.1.1 Sensitivity Run without Adsorption 

In the base model, adsorption and desorption processes are included in the simulation through a 

surface complexation model. In this section, we present results for a simulation without 

adsorption/desorption, and compare these with the base model. A simulation run without 

adsorption allows quantitative evaluation of the role of adsorption/desorption on the evolution of 

the aqueous concentration of hazardous constituents. Because of this change, the initial 

distribution of hazardous constituents in the aquifer is different. Specifically, no initial mass is 

sorbed onto mineral surfaces, which is otherwise released with the ingress of CO2 (see previous 

section). Thus, in the sensitivity run without adsorption, dissolution of galena is the only process 

mobilizing Pb and increasing the aqueous Pb concentrations, while dissolution of arsenopyrite 

(in solid solution) is the only process producing changes in aqueous As concentrations. 
 

230 of 352

In the base run, desorption in the two-phase zone releases Pb into the aqueous phase; the solution 

there becomes supersaturated with respect to galena, and consequently galena precipitates 

(Figure 4.18 right). In the sensitivity run, the increased acidity caused by the ingress of CO2 in 

turn causes dissolution of galena (Figure 4.18 left). Spatial distributions of the resulting aqueous 

Pb concentration at different times are shown in Figure 4.19. The Pb dissolved close to the 

intrusion zone migrates downstream, where galena continues to dissolve because of the lowered 

pH. As a result, the initially relatively small aqueous Pb concentrations in the two phases 

continue to build up with travel distance (Figure 4.19, left); however, the trend observed in the 

profiles is very moderate, and it is not clear whether Pb concentrations above the MCL could be 

reached much further downstream, should the simulation interval be sufficiently long. During the 

100-year period shown here, the resulting Pb concentration is smaller in the sensitivity run 

compared to the base case (Figure 4.19 right), indicating that desorption has the potential to 

release more Pb than dissolution of galena. 

0 100 200 300 400 500

Distance (m)

-0.016

-0.012

-0.008

-0.004

0

Galena volume fraction change (%)

5 yr

20 yr

50 yr

100 yr

0 100 200 300 400 500

Distance (m)

-0.1

0

0.1

0.2

0.3

0.4

Galena volume fraction change (%)

100 yr sorption

100 yr no sorption

Fig. 4.18. Galena volume fraction change profiles along x-axis at y = 0 m 0 at different times in a sensitivity run 

without adsorption/desorption (left), and comparison of galena volume fraction change profile along x 

at y=0 after 100 years of CO2 intrusion with base model (right). 
 

231 of 352

0 100 200 300 400 500

Distance (m)

0x100

2x10-8

4x10-8

6x10-8

8x10-8

Total aqueous Pb concentration (mol/L)

5 yr

20 yr

50 yr

100 yr

MCL

0 100 200 300 400 500

Distance (m)

0x100

2x10-8

4x10-8

6x10-8

8x10-8

Total aqueous Pb concentration (mol/L)

100 yr sorption

100 yr no sorption

MCL

Fig. 4.19. Aqueous Pb concentration profile along x at y=0 at different times in a sensitivity run without 

adsorption/desorption (left), and comparison of model results for total aqueous Pb concentration 

profile along x at y=0 after 100 years of CO2 intrusion with base model (right). 

The omission of adsorption/desorption processes is similarly important for As. Without 

adsorption, the As concentrations increase significantly with travel distance (Figure 4.20, left). 

The trend seen in the profiles suggests that the MCL for As would be exceeded further 

downstream, and that the maximum concentration of As would be limited by the solubility of 

arsenopyrite (see Section 3.5.3). This is in contrast to the base model, where adsorption stabilizes 

the aqueous As to a relatively uniform level that is smaller than the MCL (Figure 4.20 right). 

That the base-model results show much higher concentration values in the two-phase zone near 

the CO2 intrusion location is caused by the significant mobilization of initially sorbed As. 

0 100 200 300 400 500

Distance (m)

4.0x10-8

6.0x10-8

8.0x10-8

10-7

1.2x10-7

1.4x10-7

Total aqueous As concentration (mol/L)

5 yr

20 yr

50 yr

100 yr

MCL

0 100 200 300 400 500

Distance (m)

4.0x10-8

6.0x10-8

8.0x10-8

10-7

1.2x10-7

1.4x10-7

Total aqueous As concentration (mol/L)

100 yr sorption

100 yr no sorption

MCL

Fig. 4.20. Aqueous As concentration profile along x at y=0 at different time in a sensitivity run without 

adsorption/desorption (left) and comparison of model results of As concentration profile along x at y=0 

after 100 years of CO2 intrusion with base model (right). 
 

232 of 352

In the TOUGHREACT model, adsorption/desorption processes are calculated assuming a local 

equilibrium, which means that the surface complex responds instantaneously to pH changes. This 

is a reasonable assumption, because adsorption reactions are fast compared to the kinetically 

controlled dissolution/precipitation of hazardous mineral hosts. As a result, desorption is the 

dominant process affecting aqueous Pb and As in the short term (see Figure 4.21 for the situation 

after 1 year of CO2 intrusion). 

0 100 200 300 400 500

Distance (m)

1.2x10-9

1.4x10-9

1.6x10-9

1.8x10-9

2.0x10-9

2.2x10-9

2.4x10-9

Total aqueous Pb concentration (mol/L)

1 year sorption

1 year no sorption

0 100 200 300 400 500

Distance (m)

4.0x10-8

5.0x10-8

6.0x10-8

7.0x10-8

8.0x10-8

Total aqueous As concentration (mol/L)

1 year sorption

1 year no sorption

Fig. 4.21. Comparison of model results for sensitivity case without adsorption/desorption and base model, 

showing aqueous Pb (left) and As (right) concentration profiles along x at y=0 after 1 year of CO2

intrusion. 

We may conclude that adsorption/desorption processes are extremely important when predicting 

changes in aqueous concentrations of Pb and As (for the given mineralogical composition). 

Incorporation of these processes has two main effects: (1) initially sorbed matter will be released 

as pH changes, leading to increased concentration of Pb and As near the intrusion zone, and (2) 

adsorption of dissolved or desorbed mass tends to stabilize its downstream concentration. 

Compared to the relatively slow dissolution rate of galena or arsenopyrite, desorption acts much 

more rapidly, leading to increased local Pb or As concentrations. 
 

233 of 352

4.6.1.2 Sensitivity Run with Lower Site Density 

The role adsorption/desorption plays in the mobilization of hazardous constituents depends 

strongly on the total number of adsorption sites, which is the product of adsorption-site density 

and specific surface area of mineral. In the base run, illite is the most important mineral that 

adsorbs or desorbs both Pb and As. In the base model, the specific surface area (66.8 m2/g) and 

site density (1.3×10-6 mol/m2 for strong sites and 2.27×10-6 mol/m2 for weak sites) for this 

mineral are taken from Gu and Evans (2007). However, Lackovic et al. (2003) pointed out that 

surface properties vary widely for illites from different sources. To study the effect of 

uncertainties of surface properties of illite on the evolution of Pb and As after CO2 intrusion, we 

use the published site density (2×10-7 mol/m2 for strong sites and 6×10-7 mol/m2 for weak sites) 

from Lackovic et al. (2003) in the sensitivity run. 

In the current simulation, the chemical evolution of Pb depends on the dynamic balance of 

galena dissolution/precipitation and adsorption/desorption from mineral surfaces. If these two 

processes are considered separately, the increase in acidity triggers desorption as well as galena 

dissolution. In a coupled system, however, depending on the respective geochemical parameters, 

desorption may release sufficient Pb to the aqueous phase to precipitate galena, or in contrast, 

when galena dissolution may release sufficient Pb to the aqueous phase, such that adsorption 

takes place. In the base model, galena precipitates (Figure 4.22 right) because desorption of Pb 

from illite surface releases sufficient Pb such that the solution becomes supersaturated with 

respect to galena. In the sensitivity run, because the site density on illite is much smaller, the 

mass of Pb released from adsorption sites decreases correspondingly, the solution becomes 

undersaturated with respect to galena, and thus dissolution of this mineral occurs (Figure 4.22 

left). 

Figure 4.23 shows the spatial distribution of aqueous Pb at different times, exhibiting a very 

complex transient behavior. The aqueous Pb concentration actually decreases during the first two 

years, although galena dissolves and desorption occurs. This is because dissociation of important 

aqueous complexes affects the early-time aqueous concentration of Pb. Remember that at initial 

conditions, Pb+2 and PbCO3(aq) account for only 0.02% and 1% of the total aqueous Pb, 
 

234 of 352

respectively, while PbS(aq) accounts for about 85% (see Table 4.4). When the solution becomes 

more acidic, the complexation of PbCO3(aq) tends to generate an increase of aqueous Pb, 

whereas the dissociation of PbS(aq) tends to work against an increase of aqueous Pb, both of 

which are assumed to be equilibrium reactions in TOUGHREACT. The dissociation of PbS(aq) 

does not increase the total aqueous Pb, but releases Pb+2, which is adsorbed on mineral surfaces, 

leading to a decrease in total aqueous Pb. Since this decrease is initially not fully compensated 

by the dissolution of galena, we observe a decrease of total aqueous Pb concentration in the twozone (Figure 4.23 left), which migrates downstream with time (Figure 4.23 right, 5 and 20 

years). As time progresses, the long-term effect of dissolution of galena eventually surpasses the 

short-term effect of dissociation of PbS(aq). We observe an increase in aqueous Pb concentration 

with peaks in the two-phase zone and an elevated Pb concentration downstream, which is about 

two times the initial concentration. 

Figure 4.24 (left) shows the time evolution of total aqueous Pb concentration measured at the 

CO2 intrusion location. The early concentration decrease during the first two years is followed by 

a gradual increase up to the peak concentration observed in the profile plots. This early-time 

transient behavior is controlled by the complex interaction of the dissociation of PbS(aq), 

adsorption of Pb+2 onto mineral surfaces, dissolution of galena, transport of all Pb species 

downstream, all of which are affected by a gradually increasing partial pressure of CO2. (The 

partial pressure of CO2 increases slowly until a two-phase zone forms after about 3 years—see 

Figures 4.2 and 4.3.) Figures 4.24 (right) shows the total aqueous Pb concentration after 100 

years for the sensitivity case (in comparison with the base model). The base run leads to much 

higher Pb concentrations, because desorption is more effective and more Pb is released into 

solution. 

The results for As indicate a simpler behavior than for Pb. Without complex aqueousdissociation processes occurring, arsenopyrite dissolves in response to pH changes, while 

adsorption occurs in the two-phase region and desorption takes place further downstream. 

Because of the lower site density on illite in the sensitivity run, less As is released from 

adsorption sites, and therefore a lower As concentration is observed in the two-phase zone. 
 

235 of 352

Subsequently, less aqueous As is transported downstream, and the aqueous As concentration is 

smaller compared to the base model (Figure 4.25). 

0 100 200 300 400 500

Distance (m)

-0.02

-0.016

-0.012

-0.008

-0.004

0

Galena volume fraction change (%)

5 yr

20 yr

50 yr

100 yr

0 100 200 300 400 500

Distance (m)

-0.1

0

0.1

0.2

0.3

0.4

Galena volume fraction change (%)

100 yr base run

100 yr sensitivity run

Fig. 4.22. Galena volume fraction change profiles along x-axis at y = 0 m 0 at different times in a sensitivity run 

with lower site density for illite and comparison of galena volume fraction change profile along x at 

y=0 after 100 years of CO2 intrusion with base model (right). 

0 100 200 300 400 500

Distance (m)

6.0x10-10

8.0x10-10

10-9

1.2x10-9

1.4x10-9

Total aqueous Pb concentration (mol/L)

0.27 yr

1 yr

2 yr

0 100 200 300 400 500

Distance (m)

0x100

10-9

2x10-9

3x10-9

4x10-9

Total aqueous Pb concentration (mol/L)

5 yr

20 yr

50 yr

100 yr

Fig. 4.23. Aqueous Pb concentration profile along x at y=0 at different times in a sensitivity run with lower site 

density on illite. 
 

236 of 352

012345

Time (year)

0x100

10-9

2x10-9

3x10-9

4x10-9

Total aqueous Pb concentration (mol/L)

0 100 200 300 400 500

Distance (m)

0x100

2x10-8

4x10-8

6x10-8

8x10-8

Total aqueous Pb concentration (mol/L)

100 yr base run

100 yr sensitivity run

MCL

Fig. 4.24. Time evolution of total aqueous Pb concentration at the intrusion location (left) and comparison of 

model results for total aqueous Pb concentration profile along x at y=0 after 100 years of CO2 intrusion 

in sensitivity and base model (right). 

0 100 200 300 400 500

Distance (m)

4.0x10-8

6.0x10-8

8.0x10-8

10-7

1.2x10-7

1.4x10-7

Total aqueous As concentration (mol/L)

5 yr

20 yr

50 yr

100 yr

MCL

0 100 200 300 400 500

Distance (m)

4.0x10-8

6.0x10-8

8.0x10-8

10-7

1.2x10-7

1.4x10-7

Total aqueous As concentration (mol/L)

100 yr base run

100 yr sensitivity run

MCL

Fig. 4.25. Aqueous As concentration profile along x at y=0 at different time in a sensitivity run with lower site 

density and comparison of model results of As concentration profile along x at y=0 after 100 years of 

CO2 intrusion with base model (right). 

The sensitivity run with lower site density reveals to what extent adsorption affects the chemical 

evolution of Pb and As in relation to the total number of sites on a mineral surface. Overall, 

decreasing site density lowers aqueous Pb and As concentrations. However, the transient 

chemical evolution of Pb is affected by a dynamic balance between mineral dissolution, 

adsorption, and dissociation of aqueous complexes. 
 

237 of 352

4.6.1.3 Sensitivity to Surface Complexation Constant 

Illite is considered the most important adsorbent of As in this study, and the following surface 

complexation reaction 

3 2 33 Ill OHAsO H O H Ill OH H AsO _ _ − + ++ + U (4.5) 

is most relevant. In the base model, the surface complexation constant (logK) in reaction (4.3) is 

taken to be -2.12 (Manning and Goldberg, 1997). However, according to Goldberg (2002), the 

value of the surface complexation constant (logK) may be as low as -3. With this in mind, we 

conducted a sensitivity run using logK = -3. 

Figure 4.26 indicates that the evolution of aqueous As is quite sensitive to the change in the 

complexation constant. A decreased complexation constant corresponds to less effective 

adsorption of As and thus a higher aqueous As concentration. It should be noticed that 

adsorption/desorption is determined as the net effect of surface complexation on different 

mineral surfaces for different surface complexes. Thus, variation of the constant for one surface 

complexation reaction changes not only the concentration of its own surface complex, but also 

the concentration of other surface complexes. For example, when the constant of 3 Ill OHAsO _ −

changes from -2.12 to -3, the concentration of 3 Ill OHAsO _ − decreases (Figure 4.27 left), while the 

concentration of 2 3 Ill OH AsO _ increases (Figure 4.27 right). The fact that more As is adsorbed 

when the constant for reaction 4.3 changes is the result of variation in As related surface 

complexes on the illite, as well as on other minerals. 
 

238 of 352

0 100 200 300 400 500

Distance (m)

4.0x10-8

8.0x10-8

1.2x10-7

1.6x10-7

Total aqueous As concentration (mol/L)

100 yr base run logk = -2.12

100 yr sensitivity run logk = -3

MCL

Fig. 4.26. Computed As concentration after 100 years of CO2 intrusion in a sensitivity run (logK = -3) and base 

model (logK = -2.12) 

0 100 200 300 400 500

Distance (m)

0

0.01

0.02

0.03

0.04

ill_HAsO3

- (mol/L)

100 yr base run logK = -2.12

100 yr sensitivity run logK = -3

0 100 200 300 400 500

Distance (m)

0

0.01

0.02

0.03

0.04

ill_H AsO 2 3 (mol/L)

100 yr base run logK = -2.12

100 yr sensitivity run logK = -3

Fig. 4.27. Surface complex Ill _HAsO3

-

 (left) and Ill _H2AsO3 (right) profiles along the x-axis at y = 0 m, in a 

sensitivity run (logK = -3) and base model (logK = -2.12). 
 

239 of 352

4.6.2 Sensitivity to Kinetic Rate Parameter 

4.6.2.1 Sensitivity to Kinetic Rate Parameter for Galena 

In the current model, the dissolution of galena is mainly caused by the increase of acidity as CO2

intrudes into the aquifer. A literature search for nonoxidative rates of galena dissolution was 

conducted. Published values, determined by laboratory experiments, vary over several orders of 

magnitude, with most values obtained at pH less than 4. Zhang et al. (2003) conducted an 

experiment on dissolution kinetics of galena in 1 mol/L NaCl solutions at pH 0.43–2.45 and 25–

75 oC. The kinetic-rate constant for galena dissolution is given by the equation: 7 2.34 10 H k α +

− = ×

at 25 o

C. Acero et al. (2007) reported a galena dissolution kinetic-rate constant as: 

23 3

5.7 0.4 0.43 0.05 10 RT

H k ea +

− ±

−± ± = for pH below 2. Apart from the large variation in measured rates from 

laboratory experiments, discrepancies between field estimates and laboratory measurements of 

reaction rates have also been observed, with differences of up to four orders of magnitude 

(Brantley and Velbel, 1993). These discrepancies may result from a lack of understanding of the 

reaction mechanism, experimental errors, differences in solution chemistry, inaccuracies in 

reactive surface area and estimates of water-mineral contact area, temperature variability, or 

biological activity (Brantley and Velbel, 1993). Note that the kinetic rate (r) is a product of 

kinetic-rate constant (k) and reactive surface area (A) (see Eq. 4.1). The magnitude of the 

reactive surface area is also highly uncertain, which further aggravates the uncertainty associated 

with the kinetic rate. 

Sensitivity simulations have been performed to partially address the impact of large uncertainties 

of the kinetic rate. Below, we have varied the kinetic-rate constant by two orders of magnitude. 

Because the kinetic rate is a product of the kinetic-rate constant and the reactive surface area, a 

two-order of magnitude variation of the reactive surface area would lead to the same variation of 

the kinetic rate; therefore, we have not conducted a separate sensitivity simulation varying the 

reactive surface area. In the current simulation, the precipitation rate is assumed to be the same 

as the dissolution rate. 
 

240 of 352

As discussed in Section 4.2 for the base run, galena precipitates rather than dissolves upon 

intrusion of CO2. It follows that in the sensitivity run, a lower kinetic rate leads to less 

precipitation of galena (Figure 4.28 left). Consequently, more aqueous Pb remains in the aqueous 

phase and the concentration of Pb in the two-phase zone is slightly higher (Figure 4.28 right). 

Overall, the simulated concentrations of aqueous Pb after 100 years of CO2 intrusion are rather 

insensitive to the variation of the kinetic rate, because desorption is the dominant process that 

controls the evolution of aqueous Pb concentration, effectively dampening the effect of mineral 

precipitation. In order to illustrate how the kinetic rate would affect the aqueous Pb concentration 

with less dominant sorption, we also performed two sensitivity simulations varying the kinetic 

rate constant in the absence of sorption. Without sorption, galena dissolves, causing 

corresponding increases in aqueous Pb concentrations. An increase in the kinetic-rate constant by 

two orders of magnitude leads to more effective dissolution, which results in a slight increase of 

aqueous Pb concentration after 100 years of CO2 intrusion (see Figure 4.29) 

0 100 200 300 400 500

Distance (m)

-0.1

0

0.1

0.2

0.3

0.4

0.5

Galena volume fraction change (%)

100 yr base run K25H= 2.34x10-7

100 yr sensitivity run K25H= 2.34x10-5

100 yr sensitivity run K25H= 2.34x10-9

0 100 200 300 400 500

Distance (m)

0x100

2x10-8

4x10-8

6x10-8

8x10-8

Total aqueous Pb concentration (mol/L)

base run K25H=2.34x10-7

sensitivity run K25H=2.34x10-5

sensitivity run K25H=2.34x10-9

MCL

Fig. 4.28. Computed galena volume fraction change (left) and (right) total adsorbed Pb change after 100 years of 

CO2 intrusion with different kinetic-rate constant. 
 

241 of 352

0 100 200 300 400 500

Distance (m)

0x100

2x10-8

4x10-8

6x10-8

8x10-8

Total aqueous Pb concentration (mol/L)

base run K25H=2.34x10-7

sensitivity run K25H=2.34x10-5

sensitivity run K25H=2.34x10-9

MCL

Fig. 4.29. Sensitivity of computed Pb concentration after 100 years of CO2 intrusion to kinetic-rate constant (k) for 

simulations without considering sorption. 

4.6.2.2 Sensitivity to Kinetic Rate Parameter for Arsenopyrite 

Although the kinetic rates of arsenopyrite oxidation have been widely studied (McKibben et al., 

2008; Walker et al., 2006; Yu et al., 2007), nonoxidative rates for arsenopyrite dissolution have 

been rarely reported, and do not permit parameters estimation for the purpose of this study. In the 

current model, the kinetic-rate constant for arsenopyrite is assumed to be the same as for galena 

(Zhang et al., 2003). We conducted a sensitivity analysis varying the kinetic-rate constant for 

arsenopyrite by two orders of magnitude. 

Figure 4.30 shows the sensitivity of the resulting aqueous As concentration to the kinetic-rate 

constant. As discussed in the Section 4.2, while desorption of As from sorption sites and 

dissolution of arsenopyrite both release As into the aqueous phase, the contribution of dissolution 

is comparably small in the base run. A decrease of the kinetic-rate constant makes the 

contribution of dissolution even smaller, only marginally affecting the total release of As. An 

increase of the rate constant results in increased mobilization of As, which shows in a slightly 

higher aqueous As concentration. Overall, the model results are rather insensitive to the kinetic 

rate constant. As with Pb, the dominant process affecting aqueous concentration is adsorption/ 

desorption rather than precipitation/dissolution. For the purpose of comparison, sensitivity 
 

242 of 352

simulations were conducted for a case without sorption (Figure 4.31). For this case, a two-orderof-magnitude higher kinetic rate leads to a dramatic increase in aqueous As concentration. 

0 100 200 300 400 500

Distance (m)

4x10-8

6x10-8

8x10-8

10-7

1x10-7

1x10-7

Total aqueous As concentration (mol/L)

base run K25H=2.34x10-7

sensitivity run K25H=2.34x10-9

sensitivity run K25H=2.34x10-5

MCL

Fig. 4.30. Computed As concentration after 100 years of CO2 intrusion in sensitivity runs with different kineticrate constant (k). 

0 100 200 300 400 500

Distance (m)

10-8

10-7

10-6

10-5

Total aqueous 

As concentration (mol/L)

base run K25H=2.34x10-7

sensitivity run K25H=2.34x10-9

sensitivity run K25H=2.34x10-5

MCL

Fig. 4.31. Sensitivity of computed As concentration after 100 years of CO2 intrusion to rate constant (k) for 

simulations without considering sorption. 
 

243 of 352

4.6.3 Sensitivity to PbS(aq) Dissociation Constant 

As discussed in Sections 4.4 and 3.5.3, PbS(aq) is the most important aqueous complex at 

ambient (low) partial pressure of CO2 partial pressure; it accounts for about 85% of the total 

aqueous Pb at a logP(CO2) of -2.3. For reasons reported in preceding sections, the existence of 

Pb sulfide complexes can strongly retard solubility changes of galena when P(CO2) increases. 

This result is based on a dissociation constant (logK) of -5.5 for the following reaction: 

2 PbS aq H HS Pb K ( ) log 5.5 + −+ + + =− U (4.6) 

There is some uncertainty associated with the magnitude of the dissociation constant, as is 

discussed in Appendix D. Therefore, a sensitivity study to evaluate changes in the magnitude of 

this constant is called for, which was conducted by changing logKPbS(aq) by one order of 

magnitude up and down. 

Table 4.10 shows the concentration of some Pb aqueous species obtained from the initial 

equilibrium run. Variation of logKPbS(aq) affects the initial concentration of PbS(aq) and also the 

total Pb concentration, while other important species like Pb+2 and PbCO3 (aq) remain the same. 

When the initially equilibrated system is disturbed as a result of CO2 intrusion, important 

processes involved in the evolution of aqueous Pb concentration are association/dissociation of 

PbS(aq), desorption/adsorption of Pb+2, precipitation of galena, and dissolution of pyrite. Note 

that precipitation of galena and dissolution of pyrite are both kinetically controlled. In the short 

term (within the first 2 years), dissolution of pyrite does not produce enough HS-. Therefore, 

PbS(aq) dissociates (see Section 4.6.1.1), and the desorption of Pb+2 is consequently retarded. In 

the long term (after 5 years), dissolution of pyrite produces sufficient HS-

 so that PbS(aq) 

associates, and the desorption of Pb+2 is accelerated. As a result, the sensitivity case with 

logKPbS(aq)=-6.5 leads to greater desorption after 100 years of CO2 intrusion (see Figure 4.32 

left), while less galena precipitates, because the association of PbS(aq) retards the precipitation 

of galena (Figure 4.32 right). In summary, the case with logKPbS(aq)=-6.5 results in more 

desorption, less galena precipitation, and higher Pb concentration, both initially and after CO2

intrusion (see Figure 4.33). The opposite behavior can be seen for the case with logKPbS(aq)=-4.5. 
 

244 of 352

Table 4.10. Concentration (in mol/L) of some Pb aqueous species calculated by different dissociation constants of 

PbS(aq) at initial condition with logP(CO2) = -2.3 

Species logKPbS(aq)=-5.5 logKPbS(aq)=-6.5 logKPbS(aq)=-4.5 

PbCO3(aq) 1.50E-11 1.50E-11 1.50E-11 

Pb+2 2.06E-13 2.06E-13 2.06E-13 

PbS(aq) 1.11E-09 1.11E-08 1.11E-10 

PbSe(aq) 1.83E-10 1.83E-10 1.83E-10 

Total Pb 1.30E-09 1.13E-08 3.03E-10 

0 100 200 300 400 500

Distance (m)

-100

-80

-60

-40

-20

0

20

Total Pb sorption sites change (%)

100 yr logKPbS(aq)=-5.5

100 yr logKPbS(aq)=-6.5

100 yr logKPbS(aq)=-4.5

0 100 200 300 400 500

Distance (m)

10-1

0x100

10-1

2x10-1

3x10-1

4x10-1

Galena volume fraction change (%)

100 yr logKPbS(aq)=-5.5

100 yr logKPbS(aq)=-6.5

100 yr logKPbS(aq)=-4.5

Fig. 4.32. Computed total adsorbed Pb change (left) and galena volume fraction change (right) after 100 years of 

CO2 intrusion with different logKPbS(aq). 

0 100 200 300 400 500

Distance (m)

0x100

2x10-8

4x10-8

6x10-8

8x10-8

Total aqueous Pb concentration (mol/L)

100 yr logKPbS(aq)=-5.5

100 yr logKPbS(aq)=-6.5

100 yr logKPbS(aq)=-4.5

MCL

Fig. 4.33. Computed total aqueous Pb concentration after 100 years of CO2 intrusion with different logKPbS(aq). 
 

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4.6.4 Sensitivity to Mineral Solubility 

4.6.4.1 Sensitivity to Solubility of Galena 

The solubility of hazardous-constituent-bearing minerals, with galena and arsenopyrite (in solid 

solution) being the focus in the current model, is the most critical parameter for dissolutionrelated increases in aqueous concentrations of Pb or As. With this in mind, we conducted a 

sensitivity run in which the solubility of galena is changed. In TOUGHREACT, the dissolution 

reaction of galena is written as: 

2 PbS H HS Pb K log 14.4 + −+ + + =− U (4.7) 

The value of logK (-14.4) for reaction (4.5) has been selected based on the thermodynamic 

analysis conducted in Section 3 (Table 3.13). However, as discussed in detail in Appendix D, 

published values for logK, derived either from solubility measurements or from thermochemical 

measurements, show significant discrepancies, which were resolved through consideration of the 

neutral complex PbS(aq). However, thermochemical data used in the calibration of logK (galena) 

have uncertainties of ± 1 kcal/mol, which translate into an uncertainty in logK of about 0.5. The 

MINTEQ (Allison et al., 1991) database cites a logK value for reaction (4.5) of -13.97. In the 

sensitivity run, we arbitrarily change logK of reaction 4.5 to this value. 

As mentioned in Section 4.2, our simulations include two steps: (1) an equilibrium run, 

establishing the initial equilibrium conditions before CO2 intrusion and (2) a reactive transport 

run, simulating the chemical evolution after CO2 intrusion. Increasing the solubility of galena 

changes the initial distribution of Pb in the aqueous phase and changes the amount of Pb 

adsorbed on mineral surfaces. Table 4.11 shows that the initial total aqueous Pb concentration in 

the sensitivity run is higher (by a factor of 2.5) than that in the base model. What should be 

mentioned here is that the initial Pb concentration on adsorption sites for a given mineral surface 

(for example, illite) is affected by the initial aqueous concentration of the corresponding ion 

(Pb+2) as well as other ions (for example, H3AsO3(aq)) that participate in surface complexation 

reactions on this mineral surface (for example, ills

_OPb+). In this sensitivity run, the initial total 
 

246 of 352

number of Pb adsorption sites is higher, because the initial aqueous concentration of Pb+2 is 

higher. Consequently, more desorption occurs in the sensitivity run (see Figure 4.34 left), and the 

simulated total aqueous Pb concentration is significantly higher than that in the base run (see 

Figure 4.34 right), but still does not exceed the MCL. 

 

Table 4.11. Concentration (in mol/L) of some Pb aqueous species after initial equilibrium in base model and in 

the sensitivity run 

Species Base model Sensitivity run 

PbS(aq) 1.11E-09 2.98E-09 

PbSe(aq) 1.83E-10 1.78E-10 

PbCO3(aq) 1.77E-11 4.77E-11 

Pb+2 2E-13 5.5E-13 

Total aqueous Pb 1.30E-09 3.21E-09 

Total adsorbed Pb 5.09E-6 1.36E-5 

0 100 200 300 400 500

Distance (m)

0.0x100

4.0x10-6

8.0x10-6

1.2x10-5

1.6x10-5

total Pb sorption sites (mol/L) 

100 yr base run logKgalena= -14.4

100 yr sensitivity run logKgalena= -13.97

0 100 200 300 400 500

Distance (m)

0x100

2x10-8

4x10-8

6x10-8

8x10-8

Total aqueous Pb concentration (mol/L)

100 yr base run logKgalena= -14.4 

100 yr sensitivity run logKgalena= -13.97

MCL

Fig. 4.34. Computed total adsorbed Pb (left) and aqueous Pb concentration (right) after 100 years of CO2

intrusion in sensitivity run (logK galena = -13.97) and base model (logK galena = -14.4) 
 

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4.6.4.2 Sensitivity to Solubility of Arsenopyrite 

As pointed out before, the initial As concentration is likely controlled by arsenopyrite in solid 

solution in pyrite in most groundwaters, based on the evaluation of thermodynamic controls in 

Section 3.4. Since TOUGHREACT cannot explicitly treat the dissolution of solid solutions, 

arsenopyrite is instead incorporated in the model as a stand-alone pseudophase, possessing the 

same properties and stoichiometry as the pure end-member of arsenopyrite, but with a solubility 

product lowered to reflect the reality that the As is present in solid solution. Considering the 

uncertainties in the calibration of this solubility product—using measured groundwater analysis 

from the NWIS database (Section 3.4)—we conducted a sensitivity run in which the base model 

solubility is reduced by a one-half order of magnitude (from logK = 55.66 to logK = 55.16). 

Decreasing the solubility of arsenopyrite has two effects. First, the initial total aqueous As 

concentration is lower, which also causes the total As concentration on adsorption sites to be 

smaller. In the base model, the initial total aqueous As concentration is 4.4×10-8 mol/L and the 

initial total As concentration on adsorption sites is 3.83×10-2 mol/L, whereas the respective 

values in the sensitivity run are 1.4×10-8 mol/L and 3.78×10-2 mol/L. Second, the total aqueous 

As concentration after intrusion of CO2 is smaller than in the base model, because both 

desorption from mineral surfaces and dissolution of arsenopyrite are less significant. 

0 100 200 300 400 500

Distance (m)

0x100

4x10-8

8x10-8

1x10-7

2x10-7

Total aqueous As concentration (mol/L)

100 yr base run logKarsenopyrite= 55.66

100 yr sensitivity run logKarsenopyrite= 55.16

MCL

Fig. 4.35. Computed aqueous As concentration after 100 years of CO2 intrusion in a sensitivity run (logK 

arsenopyrite = 55.16) and base run (logK arsenopyrite = 55.66) 
 

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4.6.5 Sensitivity to Initial Mineral Volume Fraction 

4.6.5.1 Sensitivity to Initial Volume Fraction of Galena 

In the base model, the initial volume fraction of galena is set to 7.84× 10-6, based on the 

extensive discussion of minor secondary minerals described in Section 3.6.2.2. To account for 

the possible variability and to understand the impact of an increase in volume fraction, we 

conducted a sensitivity run with an initial volume fraction for galena of 7.84× 10-3. While this is 

three orders of magnitude larger than in the base model, the volume fraction is still much smaller 

than the unrealistic 50% volume fraction for galena assumed in earlier studies by Wang and Jaffe 

(2004). The increase in initial volume fraction of galena means not only that a larger amount of 

galena is available for dissolution, but also that the dissolution kinetics change, through an 

assumed increase in reactive surface area. However, as shown in Figure 4.36, the total aqueous 

Pb concentrations at 100 years are not sensitive to the volume fraction of galena. As discussed in 

Section 4.5.2, the dominant process for increasing aqueous Pb concentrations is desorption rather 

than galena dissolution, which explain why galena volume changes, within reasonable ranges 

reflecting natural variability, have no significant impact. 

0 100 200 300 400 500

Distance (m)

0x100

2x10-8

4x10-8

6x10-8

8x10-8

Total aqueous Pb concentration (mol/L)

100 yr Vgalena=7.84 x 10-6

100 yr Vgalena=7.84 x 10-3

MCL

Fig. 4.36. Sensitivity of total aqueous Pb concentration after 100 years along the x-axis at y = 0 m to the initial 

volume fraction of galena. 
 

249 of 352

4.6.5.2 Sensitivity to Initial Volume Fraction of Arsenopyrite 

The volume fraction of arsenopyrite used in the model is based on the mean abundance of As in 

soils and sediments, as reported by the National Geochemistry Survey database of the USGS, 

and used as a proxy for As abundance in aquifer host rocks (Section 3.6.2.2). It is furthermore 

assumed, as pointed out before, that arsenopyrite occurs in solid solution in pyrite. The volume 

fraction of arsenopyrite is therefore that of the FeAsS component in pyrite with an assumed ideal 

partial molar volume of unity. To account for variability and uncertainty in the magnitude of the 

volume fraction of arsenopyrite, we have increased the value chosen in the base model by two 

orders of magnitude, from about 9×10-6 to about 9×10-3. The increase in initial volume fraction 

of arsenopyrite means not only that a larger amount of arsenopyrite is available for dissolution, 

but also that the dissolution kinetics change, through an assumed increase in reactive surface 

area. Figure 4.37 shows that the total aqueous As concentrations are moderately sensitive to the 

volume fraction of arsenopyrite. Increasing the volume fraction of arsenopyrite leads to faster 

dissolution of arsenopyrite and, thus, the sensitivity results arrive at higher aqueous As 

concentrations after 100 years. The reason that As concentrations are affected by a change in the 

volume fraction of its solubility-controlling mineral host, while Pb concentrations are not (see 

earlier section), has been given before. Pb concentration increases are caused by desorption 

(while galena actually precipitates instead of dissolving), whereas As concentration increases are 

generated from both desorption and dissolution of arsenopyrite. 

0 100 200 300 400 500

Distance (m)

4.0x10-8

6.0x10-8

8.0x10-8

10-7

1.2x10-7

1.4x10-7

Total aqueous As concentration (mol/L)

100 yr Varsenopyrite=9x10-6

100 yr 100 yr Varsenopyrite=9x10-4

MCL

Fig. 4.37. Sensitivity of total aqueous As concentration after 100 years along the x-axis at y = 0 m to the initial 

volume fraction of arsenopyrite. 
 

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4.6.6 Sensitivity to Initial Partial Pressure of CO2

In the base model, the initial chemical composition is in equilibrium with a specified CO2 partial 

pressure around 0.005 bar (which corresponds to a Log P(CO2) value of -2.3). The specified 

value falls into the medium range of measured CO2 partial pressures calculated from the 

groundwater database analysis (Section 3.4.3, Figure 3.17). The high end of these measured CO2

partial pressures is on the order of –1.5 in Log P(CO2). To evaluate the impact of the higher 

initial partial pressure of CO2 in the groundwater, we conducted a sensitivity study using this 

high-end value of -1.5. First, a equilibrium run was carried out to establish the initial chemical 

composition, and then a reactive transport simulation was conducted, modeling the ingress of 

CO2 into the equilibrated system. 

Table 4.12 shows the initial chemical composition in equilibrium with a Log P(CO2) value to be 

–1.5. The increase of initial partial pressure of CO2 results in a higher bicarbonate concentration 

and a lower pH. Consistent with the equilibrium analysis in Section 3.5 and the trends shown in 

Figure 3.51, the total aqueous Pb concentration increases slightly compared to the base model, 

due to the increase in PbCO3(aq), while the initial As concentration in equilibrium with a Log 

P(CO2) value of –1.5 is significantly higher. In fact, the equilibrium concentration for As is 

approximately 1.4× 10-7 mol/L, which exceeds the MCL of 1.33× 10-7 mol/L (or 10 ppb). It is 

possible that samples with elevated ambient CO2 concentrations could be responsible for some 

occurrences of high As concentrations in groundwaters, as shown in Figure 3.6(b). 

We shall now compare the Pb concentration profiles for base model and sensitivity run at 100 

years of CO2 intrusion, shown in Figures 4.34. In the base model (initial Log P(CO2) = -2.3), the 

Pb concentration increases with the ingress of CO2 from 1.3×10-10 mol/L to about 1.57×10-8

mol/L in the two-phase zone near the intrusion location, and to about 1.1×10-8 mol/L 

downstream. In the sensitivity run (initial Log P(CO2) = -1.5), the aqueous Pb concentration 

increases from initially 1.4×10-9 mol/L to about 1.55×10-8 mol/L near the intrusion location and 

to 1.15×10-8 mol/L downstream. Thus, in absolute values, the situation after CO2 intrusion is 
 

251 of 352

almost identical in both cases; only the starting point is slightly different, owing to differences in 

the initial Pb concentration (Figure 4.38). 

Table 4.12. Initial chemical composition (mol/L) of groundwater in equilibrium with partial pressures of -2.3 (base 

model) and -1.5 (sensitivity run) in Log P(CO2) 

Species Partial pressure of CO2

Log P(CO2) = -2.3 

Partial pressure of CO2

Log P(CO2) = - 1.5 

Eh -0.215 (volt) -0.195 (volt) 

pH 7.61 7.36 

Ca 9.00E-04 4.98E-04 

Mg 2.17E-05 9.62E-05 

Na 1.97E-03 1.97E-03 

K 2.68E-04 3.85E-04 

Fe 5.63E-06 2.31E-05 

Si 9.31E-04 9.18E-04 

TIC 3.33E-03 1.26E-02 

SO4

-2 1.95E-04 1.92E-04 

HS- 1.44E-08 1.36E-08 

Al 4.19E-11 2.92E-11 

Cl 2.07E-04 2.07E-04 

P 1.78E-06 6.72E-06 

Pb 1.30E-09 1.40E-09 

As 4.42E-08 1.43E-07 

Ba 1.04E-07 1.04E-07 

Hg 8.37E-10 9.77E-10 

Sb 2.10E-09 2.05E-09 

U 7.18E-11 4.18E-11 

Zn 1.15E-08 1.22E-08 

Se 1.72E-09 1.86E-09 

Cd 1.80E-09 1.29E-09 

As mentioned before, the increase in Pb concentration caused by CO2 intrusion is mainly 

controlled by two processes: adsorption/desorption of Pb on adsorption sites and galena 

precipitation/dissolution. Although the aqueous Pb concentrations after 100 years are almost 
 

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identical in the two simulation cases, the underlying processes affecting them differ somewhat. 

The total Pb concentration on adsorption sites is lower in the sensitivity run (see Figure 4.39 

left), because of the smaller concentration of illw_OPb+

 (Figure 4.39 right). Less desorption in 

the sensitivity run induces less precipitation of galena (see Figure 4.40 left), while the computed 

pH is similar in both cases (Figure 4.40 right). 

0 100 200 300 400 500

Distance (m)

0x100

2x10-8

4x10-8

6x10-8

8x10-8

Total aqueous Pb concentration (mol/L)

100 yr initial Pco2=10-2.3

100 yr initial Pco2=10-1.5

MCL

0 100 200 300 400 500

Distance (m)

10-9

10-8

10-7

Total aqueous Pb concentration (mol/L)

100 yr initial Pco2=10-2.3

100 yr initial Pco2=10-1.5

MCL

Fig. 4.38. Sensitivity of Pb concentration to initial partial pressure of CO2. The figure on the right is the same as 

the one on the left but in semi-log scale. 

0 100 200 300 400 500

Distance (m)

10-6

2x10-6

3x10-6

4x10-6

5x10-6

6x10-6

Total Pb sorption sites (mol/L)

100 yr initial Pco2=10-2.3

100 yr initial Pco2=10-1.5

0 100 200 300 400 500

Distance (m)

0x100

10-6

2x10-6

3x10-6

4x10-6

5x10-6

6x10-6

illw_OPb+ (mol/L)

100 yr initial Pco2=10-2.3

100 yr initial Pco2=10-1.5

Fig. 4.39. Total Pb concentration on adsorption sites (left) and concentration of illw_OPb+ (right) along x-axis at 

y = 0 m in the base and sensitivity run. 
 

253 of 352

0 100 200 300 400 500

Distance (m)

-0.1

0

0.1

0.2

0.3

0.4

Galena volume fraction change (%)

100 yr Pco2=10-2.6

100 yr Pco2=10-1.5

0 100 200 300 400 500

Distance (m)

5.6

6

6.4

6.8

7.2

7.6

8

pH

100 yr initial Pco2=10-2.6

100 yr initial Pco2=10-1.5

Fig. 4.40. Galena volume fraction change (left) and pH (right) along x-axis at y = 0 m in the base and sensitivity 

run. 

Figure 4.41 shows concentration profiles of total aqueous As after 100 years in the sensitivity run 

(initial Log P(CO2) = -1.5) and the base run (initial Log P(CO2) = -1.5). The increase in initial 

Log P(CO2) results in increases of comparable magnitude in both the initial As concentration and 

the final As concentration after intrusion of CO2. Thus, the main difference between the two 

simulation cases is the initial As concentration, which is much higher in the sensitivity run using 

a Log P(CO2) of -1.5. As mentioned above, the initial As concentration is already higher than the 

MCL; the ingress of CO2 leads to a further increase (Figure 4.41). 

0 100 200 300 400 500

Distance (m)

0x100

10-7

2x10-7

3x10-7

4x10-7

Total aqueous As concentration (mol/L)

100 yr Pco2= 10-2.3

100 yr Pco2= 10-1.5

MCL

Fig. 4.41. Sensitivity of As concentration to initial partial pressure of CO2. 
 

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4.6.7 Sensitivity to Initial Fluid Pressure (Depth of Aquifer) 

In the base model, the initial pressure in the aquifer is set to 5 bar, corresponding to an aquifer at 

a depth of about 50 m. Because the solubility of CO2 in water is a function of CO2 partial 

pressure, a change in the initial aquifer pressure controls, upon intrusion of CO2, how much of 

the gaseous CO2 dissolves in the groundwater (and may affect pH) versus how much remains in 

gas phase and migrates under buoyant forces. In a shallower aquifer, the maximum amount of 

dissolved CO2 would be smaller than in a deeper aquifer, and vice versa. To understand the 

implications of this dependence, we conducted a sensitivity run assuming CO2 intrusion at 3 bar 

initial pressure (i.e., equivalent to an aquifer depth of about 30 m). All other model assumptions 

and parameters are identical to the base model. 

Figure 4.42 shows a slightly larger two-phase zone forms in the sensitivity run with a slightly 

higher gas saturation (Figure 4.42 left), while less CO2 is dissolved in the water (Figure 4.42 

right). As a result, the pH obtained in the sensitivity run (fluid pressure of 3 bar) is higher than in 

the base run (Figure 4.43). Consequently, the Pb and As concentrations (Figure 4.40) observed 

after 100 years of CO2 intrusion are slightly lower in the sensitivity run due, in the case of Pb, 

not only to the higher pH, but also to less complexing of dissolved CO2 with Pb+2. In summary, 

the geochemical impact of CO2 intrusion into a drinking water aquifer is moderately affected by 

initial fluid pressure (or aquifer depth), with smaller effects expected in a shallower aquifer. A 

drinking water aquifer would have to be significantly deeper (resulting in a higher maximum 

amount of dissolved CO2), and a CO2 would have to leak into the aquifer at a higher rate 

(resulting in an increase in dissolved CO2 up to or exceeding the maximum amount) to be exhibit 

stronger effects from CO2 intrusion and related pH changes. 
 

255 of 352

0 100 200 300 400 500

Distance (m)

0

0.02

0.04

0.06

0.08

0.1

CO2 gas saturation degree

100 yr depth = 50 m

100 yr depth = 30 m

0 100 200 300 400 500

Distance (m)

0x100

4x10-2

8x10-2

1x10-1

2x10-1

TIC (mol/L)

100 yr depth = 50 m

100 yr depth = 30 m

Fig. 4.42. Sensitivity of CO2 gas saturation degree (left) and total inorganic carbon (TIC) to the depth of aquifer. 

0 100 200 300 400 500

Distance (m)

5.6

6

6.4

6.8

7.2

7.6

8

pH

100 yr depth = 50 m

100 yr depth = 30 m

Fig. 4.43. Sensitivity of pH to the depth of aquifer. 

0 100 200 300 400 500

Distance (m)

0x100

2x10-8

4x10-8

6x10-8

8x10-8

Total aqueous Pb concentration (mol/L)

100 yr depth = 50 m

100 yr depth = 30 m

MCL

0 100 200 300 400 500

Distance (m)

4.0x10-8

6.0x10-8

8.0x10-8

10-7

1.2x10-7

1.4x10-7

Total aqueous 

As concentration (mol/L)

100 yr depth = 50 m

100 yr depth = 30 m

MCL

Fig. 4.44. Sensitivity of Pb (left) and As concentration (right) to the depth of aquifer. 
 

256 of 352

4.6.8 Sensitivity to Groundwater Flow Velocity 

In all simulation cases conducted so far, we have assumed a groundwater flow velocity of 10 

m/year (transport velocity). A sensitivity run with lower flow rate of 1 m/year was performed to 

study the effect of the groundwater flow conditions on the evolution of hazardous constituents. 

When the flow velocity decreases (while the CO2 intrusion rate remains the same), an enlarged 

two-phase zone is observed compared to the base model (see Figure 4.45). The two-phase zone 

is larger because less fresh water flows past the intrusion zone and thus less CO2 can dissolve in 

the water. The highest CO2 gas saturation degree is 0.14 in the sensitivity run, whereas in the 

base run it is 0.08 (Figure 4.47 left). The corresponding area with elevated TIC is larger in the 

sensitivity run compared to the base run (Figure 4.46 and Figure 4.47 right). Because 

groundwater flow is much slower, the enhanced TIC front has not advanced sufficiently to 

intersect the right boundary of the simulated zone after 100 years. (Note that the larger two-phase 

zone observed in this simulation case raises the question as to whether the assumption of depthaveraged processes remains valid, as the gaseous CO2 region is more pronounced. We refer to 

Section 4.9 for a three-dimensional test case to address this issue.) 

X

Y

100 200 300 400

50

100

150

200

SG

0.14

0.13

0.12

0.11

0.10

0.09

0.08

0.07

0.06

0.05

0.05

0.04

0.03

0.02

0.01

Fig. 4.45. Spatial distribution of saturation of CO2 in a sensitivity run with flow rate of 1m/year after 100 years of 

intrusion 
 

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X

Y

100 200 300 400

50

100

150

200

TIC

0.21

0.19

0.18

0.16

0.15

0.14

0.12

0.11

0.10

0.08

0.07

0.06

0.04

0.03

0.02

Fig. 4.46. Spatial distribution of TIC in a sensitivity run with flow rate of 1m/year after 100 years of intrusion 

0 100 200 300 400 500

Distance (m)

0

0.04

0.08

0.1

0.2

CO2 gas saturation degree

100 yr flow rate = 10 m/y

100 yr flow rate = 1 m/y

0 100 200 300 400 500

Distance (m)

0.0x100

5.0x10-2

10-1

1.5x10-1

2.0x10-1

2.5x10-1

TIC (mol/L)

100 yr flow rate = 10 m/y

100 yr flow rate = 1 m/y

Fig. 4.47. Comparison of CO2 gas saturation degree (left) and TIC (right) along x-axis at y = 0 m in the 

sensitivity case and the base run 

When the groundwater flow velocity decreases to 1 m/year, diffusion rather than just advection 

becomes important in controlling the transport of aqueous species. Figure 4.48 (left) shows the 

evolution of pH at different times. As a result of diffusion, the low pH profile moves not only 

downstream with the groundwater flow, but also in the upstream direction (right-hand side of the 

figure). Elevated aqueous Pb and As concentrations correspond to the areas where pH is low 

(Figure 4.49 and 4.50). We may conclude that for a case with low groundwater velocity, the 

impact of CO2 intrusion can be more significant, but is also more localized. 
 

258 of 352

0 100 200 300 400 500

Distance (m)

5.5

6

6.5

7

7.5

8

pH

5 yr

20 yr

50 yr

100 yr

0 100 200 300 400 500

Distance (m)

5.5

6

6.5

7

7.5

8

pH

100 yr flow rate = 10 m/y

100 yr flow rate = 1 m/y

Fig. 4.48. Evolution of pH at different times (left) along the x-axis at y = 0 m where groundwater flow velocity is 

1m/year, and sensitivity of pH to groundwater flow velocity after 100 years (right) 

0 100 200 300 400 500

Distance (m)

0x100

2x10-8

4x10-8

6x10-8

8x10-8

Total aqueous Pb concentration (mol/L)

5 yr

20 yr

50 yr

100 yr

MCL

0 100 200 300 400 500

Distance (m)

0x100

2x10-8

4x10-8

6x10-8

8x10-8

Total aqueous Pb concentration (mol/L)

100 yr flow rate = 10 m/y

100 yr flow rate = 1 m/y

MCL

Fig. 4.49. Total aqueous Pb concentration at different times (left) along the x-axis at y = 0 m where groundwater 

flow velocity is 1m/year, and sensitivity to groundwater flow velocity after 100 years (right) 

0 100 200 300 400 500

Distance (m)

4.0x10-8

6.0x10-8

8.0x10-8

10-7

1.2x10-7

1.4x10-7

Total aqueous As concentration (mol/L)

5 yr

20 yr

50 yr

100 yr

MCL

0 100 200 300 400 500

Distance (m)

4.0x10-8

6.0x10-8

8.0x10-8

10-7

1.2x10-7

1.4x10-7

Total aqueous As concentration (mol/L)

100 yr flow rate = 10 m/y

100 yr flow rate = 1 m/y

MCL

Fig. 4.50. Total aqueous As concentration at different times (left) along x-axis at y = 0 m where groundwater 

flow velocity is 1m/year and sensitivity to groundwater flow velocity after 100 years (right). 
 

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4.6.9 Sensitivity to CO2 Leakage Rate 

The base model assumes a gaseous CO2 intrusion rate of 7.5× 10-5 kg/s, which corresponds to 

2.36 tonnes of CO2 per year. This rate was selected based on the amount of CO2 that can dissolve 

in the aquifer without forming a large two-phase zone. To understand the implications of more 

CO2 leaking into the aquifer, we performed a sensitivity run with a CO2 intrusion rate of 6× 10-4

kg/s. When the CO2 intrusion rate increases, we observe a much larger two-phase region (see 

Figure 4.51 and Figure 4.53 left) and a correspondingly larger region with elevated TIC (see 

Figure 4.52 and Figure 4.53, right). As a result, in this two-phase zone, the CO2 partial pressure 

is larger than in the base run and the pH is lower (Figure 4.54, right). This pH decrease has 

implications for the mobilization of hazardous constituents, giving rise to significant increases in 

aqueous Pb concentrations, (Figure 4.55, right), and moderate increases in As concentrations 

downstream (Figure 4.56, right). Another reason for the significant increase of total aqueous Pb 

concentration is that the Pb carbonate aqueous complex, PbCO3(aq), becomes the most important 

aqueous species containing Pb. An increase of the carbonate concentration gives rise to an 

increase of Pb carbonate aqueous complex and thus elevates the total aqueous Pb concentration. 

Even more so than in the previous section, the large two-phase zone forming in this sensitivity 

case implies that the assumption of depth-averaged processes may not be valid for the model 

being studied. Because of buoyancy, CO2 would tend to accumulate under the confining layer at 

the top of the aquifer, possibly bypassing much of the groundwater flow in the aquifer. See 

Section 4.9 for a three-dimensional simulation of this sensitivity case. 
 

260 of 352

X

Y

100 200 300 400

50

100

150

200

SG

0.18

0.17

0.15

0.14

0.13

0.12

0.11

0.09

0.08

0.07

0.06

0.05

0.04

0.02

0.01

Fig. 4.51. Spatial distribution of saturation for CO2 in a sensitivity run with CO2 intrusion rate of 6× 10-4 kg/s after 

100 years of intrusion 

X

Y

100 200 300 400

50

100

150

200

TIC

0.22

0.20

0.19

0.17

0.16

0.14

0.13

0.12

0.10

0.09

0.07

0.06

0.04

0.03

0.02

Fig. 4.52. Spatial distribution of TIC in a sensitivity run with CO2 intrusion rate of 6× 10-4 kg/s after 100 years of 

intrusion 
 

261 of 352

0 100 200 300 400 500

Distance (m)

0

0.04

0.08

0.12

0.16

0.2

CO2 gas saturation degree

100 yr CO2 intrusion rate = 7.5x10-5 kg/s

100 yr CO2 intrusion rate = 6x10-4 kg/s

0 100 200 300 400 500

Distance (m)

0

0.05

0.1

0.15

0.2

0.25

TIC (mol/L)

100 yr CO2 intrusion rate = 7.5x10-5 kg/s

100 yr CO2 intrusion rate = 6x10-4 kg/s

Fig. 4.53. Comparison of CO2 gas saturation degree (left) and dissolved CO2 mass fraction (right) along x-axis at 

y = 0 m in the sensitivity and base run. 

 

0 100 200 300 400 500

Distance (m)

5.5

6

6.5

7

7.5

8

pH

5 yr

20 yr

50 yr

100 yr

0 100 200 300 400 500

Distance (m)

5.5

6

6.5

7

7.5

8

pH

100 yr CO2 intrusion rate = 7.5x10-5 kg/s

100 yr CO2 intrusion rate = 6x10-4 kg/s

Fig. 4.54. pH evolution at different times (left) along the x-axis at y = 0 m for CO2 intrusion rate of 6×10-4 kg/s, 

and sensitivity of pH to CO2 intrusion rate. 
 

262 of 352

0 100 200 300 400 500

Distance (m)

0x100

2x10-8

4x10-8

6x10-8

8x10-8

Total aqueous Pb concentration (mol/L)

5 yr

20 yr

50 yr

100 yr

MCL

0 100 200 300 400 500

Distance (m)

0x100

2x10-8

4x10-8

6x10-8

8x10-8

Total aqueous Pb concentration (mol/L)

100 yr CO2 intrusion rate = 7.5x10-5 kg/s

100 yr CO2 intrusion rate = 6x10-4 kg/s

MCL

Fig. 4.55. Total aqueous Pb concentration at different times (left) along the x-axis at y = 0 m for CO2 intrusion 

rate of 6×10-4 kg/s, and sensitivity of Pb concentration to CO2 intrusion rate. 

0 100 200 300 400 500

Distance (m)

4.0x10-8

6.0x10-8

8.0x10-8

10-7

1.2x10-7

1.4x10-7

Total aqueous 

As concentration (mol/L)

5 yr

20 yr

50 yr

100 yr

MCL

0 100 200 300 400 500

Distance (m)

4.0x10-8

6.0x10-8

8.0x10-8

10-7

1.2x10-7

1.4x10-7

Total aqueous 

As concentration (mol/L)

100 yr CO2 intrusion rate = 7.5x10-5 kg/s

100 yr CO2 intrusion rate = 6x10-4 kg/s

MCL

Fig. 4.56. Total aqueous As concentration at different times (left) along the x-axis at y = 0 m for CO2 intrusion 

rate of 6×10-4 kg/s, and sensitivity of As concentration to CO2 intrusion rate. 
 

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4.7 Results for Other Mineralogies 

The base run and all sensitivity cases conducted thus far have all assumed the basic mineralogy 

of a Coastal Plain Sandstone aquifer, as introduced in Section 3.6.3.1. This mineralogy is 

representative of an impure arenite, with a composition mostly consisting of quartz plus some 

feldspars, lithic fragments, and clays. Below, we evaluate other basic mineralogies, otherwise 

simulating the base model setup described in Section 4.2. The first is representative of an almost 

pure arenite typical of the mineralogy of a St Peter Sandstone aquifer, and the second is the 

mineralogy of a dolomitic limestone aquifer. The St Peter Sandstone, which consists primarily of 

quartz sand, has a poor pH buffering capacity and is presumed to be the type of aquifer most 

vulnerable to mobilization of hazardous inorganic constituents through CO2 intrusion. The 

dolomitic limestone, which is dominated by calcite and dolomite, should have good pH buffering 

capacity and is assumed to be least vulnerable to the mobilization of hazardous constituents 

during CO2 intrusion. All basic mineral compositions contain the same volumetric fractions of 

trace minerals containing metals or As as essential components (Section 3.6.2.2). 

4.7.1 St Peter Sandstone 

The mineralogy of the St Peter Sandstone, Illinois, as presented in Section 3.6.3.3, is an example 

of an aquifer with limited pH buffering capability. The most obvious feature of this mineralogy 

is that it consists mainly of quartz sand with small amounts of calcite and dolomite, both of 

which are important pH buffering minerals. The clay matrix content is very small. Table 4.13 

lists the mineralogical composition and gives the kinetic properties for minerals in this aquifer. 

An equilibrium run reveals the initial chemical composition in equilibrium with St Peter 

Sandstone before intrusion of CO2 (Table 4.14), which has a lower pH and Eh compared with the 

Coastal Plain Sandstone aquifer assumed in the base model. Slightly larger initial As aqueous 

concentrations are obtained, while the initial Pb concentration is virtually identical. Overall, the 

differences in the initial conditions are not significant. 
 

264 of 352

Table 4.13. Mineral volume fractions, possible secondary minerals, and kinetic properties for a St Peter 

Sandstone 

Parameters for Kinetic Rate Law 

Neutral 

Mechanism 

Acid Mechanism Base Mechanism 

Mineral Volume 

Fraction 

of solid 

 

A 

(cm2

/g) 

k25 

(mol/m2

/s) 

Ea

(KJ 

/mol) 

k25 Ea n(H+) k25 Ea n(H+) 

Primary: 

Quartz 0.926778 9.8 1.023×10-14 87.7 

K-feldspar 0.0505281 9.8 3.890×10-13 38 8.710×10-11 51.7 0.5 6.310×10-12 94.1 -0.823 

Oligoclase 0.00244551 9.8 1.445×10-12 69.8 2.138×10-10 65 0.457 

Kaolinite 0.00099595 1.95×105 6.918×10-14 22.2 4.898×10-12 65.9 0.777 8.913×10-18 17.9 -0.472 

Smectite-ca 0.0023423 5.64×105 1.660×10-13 35 1.047×10-11 23.6 0.34 3.020×10-17 58.9 -0.4 

Illite 0.00374012 6.68×105 1.660×10-13 35 1.047×10-11 23.6 0.34 3.020×10-17 58.9 -0.4 

Chlorite 0.00171185 9.8 3.02×10-13 88 7.762×10-12 88 0.5 

Kerogen-os 0.00238641 9.8 3.02×10-13 88 7.762×10-12 88 0.5 

Pyrite 0.000515 12.9 2.52×10-12 62.76 2.34×10-7 43.54 1 

dolomite 0.0045 12.9 2.52×10-12 62.76 2.34×10-7 43.54 1 

Calcite 0.00095337 Assumed at equilibrium 

Goethite 0.00060538 1.47×105 2.52×10-12 62.76 2.34×10-7 43.54 1 

Apatite 0.00220978 12.9 2.34×10-7 43.54 1 

Barite 0.00016334 12.9 2.34×10-7 43.54 1 

Arsenopyrite 9.0594E-06 12.9 2.34×10-7 43.54 1 

Greenockite 3.0363E-06 12.9 2.34×10-7 43.54 1 

Cinnabar 1.4640E-08 12.9 2.34×10-7 43.54 1 

Galena 7.8529E-06 12.9 2.34×10-7 43.54 1 

Gudmundite 3.89×10-7 12.9 2.34×10-7 43.54 1 

Tiemannite 1.22×10-7 12.9 2.34×10-7 43.54 1 

Uraninite 8.015×10-7 12.9 2.34×10-7 43.54 1 

Sphalerite 9.42 ×10-5 12.9 2.34×10-7 43.54 1 

Secondary: 

Dolomite 12.9 2.52×10-12 62.76 2.34×10-7 43.54 1 

Magnesite 9.8 4.571×10-10 23.5 4.169×10-7 14.4 1 

Ankerite 9.8 1.260×10-9 62.76 6.457×10-4 36.1 0.5 

Dawsonite 9.8 1.260×10-9 62.76 6.457×10-4 36.1 0.5 

Na-smectite 151.6 1.660×10-13 35 1.047×10-11 23.6 0.34 3.020×10-17 58.9 -0.4 

Pyromorphite 12.9 2.34×10-7 43.54 1 

Ferroselite 12.9 2.34×10-7 43.54 1 

Clausthalite 12.9 2.34×10-7 43.54 1 
 

265 of 352

Table 4.14. Initial chemical composition (mol/L) of groundwater in equilibrium with Coastal Plain Sandstone and 

St Peter Sandstone 

Species Base Model with Coastal 

Plain Sandstone 

St Peter Sandstone 

Eh -0.215 (volt) -0.2 (volt) 

pH 7.61 7.45 

Ca 9.00E-04 1.94E-03 

Mg 2.17E-05 4.23E-05 

Na 1.97E-03 1.97E-03 

K 2.68E-04 4.17E-04 

Fe 5.63E-06 8.45E-06 

Si 9.31E-04 6.35E-04 

TIC 3.33E-03 2.38E-03 

SO4

-2 1.95E-04 1.97E-04 

HS- 1.44E-08 1.37E-08 

Al 4.19E-11 6.58E-11 

Cl 2.07E-04 2.07E-04 

P 1.78E-06 1.04E-06 

Pb 1.30E-09 1.30E-09 

As 4.42E-08 5.44E-08 

Ba 1.12E-06 1.27E-06 

Cd 1.76E-09 1.41E-09 

Hg 2.53E-09 2.52E-09 

Sb 3.57E-10 4.19E-10 

Se 1.69E-08 1.67E-08 

U 4.48E-09 3.15E-09 

Zn 3.40E-07 3.51E-07 

Intrusion of CO2 into the St Peter Sandstone aquifer gives rise to geochemical changes similar to 

those observed in the base model. With gaseous CO2 dissolving into the aqueous phase, the pH 

drops and mobilization of hazardous constituents occurs. Figure 4.57 (left) shows the simulated 

pH in profiles along the x-axis at y=0 m for different times. Note that the pH in the two-phase 

zone close to the intrusion location is significantly smaller at 100 years than at 50 years. Slight 
 

266 of 352

decreases in pH can also be seen along the first 100 m downstream of the two-phase zone. This 

further pH drop is caused by the partial loss of pH buffer capability as the initially small volumes 

of calcite and dolomite minerals dissolve and eventually become depleted in the two-phase area 

and the immediate downstream region. 

Figure 4.58 (left) shows the calcite volume fraction changes at different times. After 20 years, a 

small region of complete calcite depletion develops in the two-phase zone, which means that 

calcite ceases acting as a pH buffer. With time, this region extends further downstream. The 

dissolution of dolomite (Figure 4.59 right) is less rapid; dolomite is fully depleted only in a small 

region, after about 100 years. In the two-phase region, where both calcite and dolomite are fully 

depleted at the end of the simulation period, the pH decreases to about 4.5 at 100 years (Figure 

4.57). In the region immediately downstream, where calcite is depleted, but dolomite is still 

present, the pH value is slightly lower than further downstream. As shown in Figure 4.57 (right), 

the regions with complete dissolution of calcite/dolomite correspond to the regions where the St 

Peter Sandstone and the Coastal Plain Sandstone differ most with respect to pH. 

0 100 200 300 400 500

Distance (m)

4

5

6

7

8

pH

5 yr

20 yr

50 yr

100 yr

0 100 200 300 400 500

Distance (m)

4

5

6

7

8

pH

100 yr Coastal Plain Sandstone

100 yr St Peter Sandstone

Fig. 4.57. pH profiles (left) along the x-axis at y = 0 m at different times for St Peter sandstone, and comparison 

of pH computed for Coastal Plain Sandstone and St Peter Sandstone (right) 
 

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0 100 200 300 400 500

Distance (m)

-100

-80

-60

-40

-20

0

20

Calcite volume fraction change (%)

5 yr

20 yr

50 yr

100 yr

0 100 200 300 400 500

Distance (m)

-100

-80

-60

-40

-20

0

20

Dolomite volume fraction change (%)

5 yr

20 yr

50 yr

100 yr

Fig. 4.58. Volume fraction of calcite (left) and dolomite (right) along x-axis at y = 0 m 

Figures 4.55 (left) shows the resulting Pb concentrations for the St Peter Sandstone at different 

times after CO2 intrusion, exhibiting a complex transient behavior. Early on, the Pb 

concentrations increase strongly in the two-phase zone, as observed in the base model, but 

decrease at later stages. From the observed evolution of pH, which decreases at later stages 

because buffering minerals become depleted, one would have expected the opposite behavior. 

Analysis of the adsorption characteristics for Pb reveals that the trend of decreasing Pb 

concentrations in the two-phase zone is caused by a corresponding depletion of Pb on adsorption 

sites (Figures 4.56). Similar to the base model, the early increase in aqueous Pb is mainly caused 

by desorption of initially sorbed Pb. Since the St Peter sandstone contains less clay mineral than 

the Coastal Plain Sandstone, the initial amount of sorbed Pb is smaller and thus becomes fully 

depleted in the two-phase zone after about 50 years, taking away a significant source of Pb in 

solid phase. 

Figures 4.55 (right) shows a comparison of Pb concentrations after 100 years of CO2 intrusion 

for the St Peter Sandstone and Coastal Plain Sandstone, respectively. The sensitivity run has 

smaller Pb concentrations in and near the two-phase zone, because of the above-discussed 

depletion of sorbed Pb. In contrast, the area further downstream shows slightly higher aqueous 

Pb concentrations for the St Peter Sandstone, apparently caused by the less effective readsorption of Pb migrating in downstream direction. We may conclude that the differences in Pb 
 

268 of 352

concentrations observed in the sensitivity run compared to the base model are mostly related to 

differences in the adsorption/desorption characteristics (the clay content is different), while the 

differences in the pH buffer characteristics play a lesser role. 

A different behavior is seen in the As results. The As concentration in the groundwater increases 

strongly in the two-phase zone between 50 years to 100 years (Figure 4.61 left), consistent with 

the further decrease of pH during this period. The obvious difference to the results observed for 

Pb is that there is enough As initially sorbed on mineral sites such that depletion of sorbed matter 

is not an issue. In fact, the plot showing the total As concentration on adsorption sites at 100 

years in Figure 4.62 (left) is a straight line (for both the St Peter Sandstone and Coastal Plain 

Sandstone) because the spatial variation is too small to be detected. That significant desorption 

of As occurs in the two-phase zone between 50 and 100 years is evident in Figure 4.62 (right), 

which shows the change in sorbed As compared to the initial conditions. The resulting 

differences between the sensitivity case and the base model (Figure 4.61 right) are consistent 

with the observed differences in pH within and near the two-phase zone. The St Peter Sandstone 

aquifer arrives at higher As concentration than the Coastal Plain Sandstone because (1) As 

desorbs more strongly and (2) dissolution of arsenopyrite is enhanced. 

0 100 200 300 400 500

Distance (m)

0.0x100

2.0x10-8

4.0x10-8

6.0x10-8

8.0x10-8

Total aqueous Pb concentration (mol/L)

5 yr

20 yr

50 yr

100 yr

MCL

0 100 200 300 400 500

Distance (m)

0.0x100

2.0x10-8

4.0x10-8

6.0x10-8

8.0x10-8

Total aqueous Pb concentration (mol/L)

100 yr Coastal Plain Sandstone

100 yr St Peter Sandstone

MCL

Fig. 4.59. Total aqueous Pb concentration profiles (left) along x-axis at y = 0 m at different times for St Peter 

Sandstone, and comparison of computed Pb concentration for Coastal Plain Sandstone and St Peter 

Sandstone with adsorption/desorption (right) 
 

269 of 352

0 100 200 300 400 500

Distance (m)

0.0x100

2.0x10-6

4.0x10-6

6.0x10-6

Total Pb sorption sites (mol/L)

100 yr Coastal Plain Sandstone

100 yr St Peter Sandstone

0 100 200 300 400 500

Distance (m)

-100

-80

-60

-40

-20

0

20

Total Pb sorpiton sites change (%)

50 yrs

100 yrs

Fig. 4.60. Comparison of total Pb concentration on adsorption sites in mol/L for Coastal Plain Sandstone and St 

Peter Sandstone (left), and relative change in concentration on adsorption sites for St Peter Sandstone 

at 50 and 100 years (right) 

0 100 200 300 400 500

Distance (m)

0.0x100

10-7

2.0x10-7

3.0x10-7

4.0x10-7

Total aqueous As concentration (mol/L)

5 yr

20 yr

50 yr

100 yr

MCL

0 100 200 300 400 500

Distance (m)

0.0x100

10-7

2.0x10-7

3.0x10-7

4.0x10-7

Total aqueous As concentration (mol/L)

100 yr Coastal Plain Sandstone

100 yr St Peter Sandstone

MCL

Fig. 4.61. Total aqueous As concentration profiles (left) along x-axis at y = 0 m at different times for St Peter 

Sandstone, and comparison of computed As concentration for Coastal Plain Sandstone and St Peter 

Sandstone (right) 
 

270 of 352

0 100 200 300 400 500

Distance (m)

0.0x100

10-2

2.0x10-2

3.0x10-2

4.0x10-2

Total As sorption sites (mol/L)

100 yr Coastal Plain Sandstone

100 yr St Peter Sandstone

0 100 200 300 400 500

Distance (m)

-0.3

-0.2

-0.1

0

0.1

Total As sorption sites change (%)

50 yrs

100 yrs

Fig. 4.62. Comparison of total As concentration on adsorption sites in mol/L for Coastal Plain Sandstone and St 

Peter Sandstone (left), and relative change in concentration on adsorption sites for St Peter Sandstone 

at 50 and 100 years (right) 

0 100 200 300 400 500

Distance (m)

10-9

2.0x10-9

3.0x10-9

4.0x10-9

Total aqueous Pb concentration (mol/L)

5 yr

20 yr

50 yr

100 yr

0 100 200 300 400 500

Distance (m)

4.0x10-8

6.0x10-8

8.0x10-8

10-7

1.2x10-7

1.4x10-7

1.6x10-7

Total aqueous As concentration (mol/L)

5 yr

20 yr

50 yr

100 yr

MCL

Fig. 4.63. Total aqueous Pb (left) and As (right) concentration profiles (left) along x-axis at y = 0 m at different 

times for St Peter Sandstone obtained by a simulation without adsorption/desorption 
 

271 of 352

4.7.2 Dolomitic Limestone 

A dolomitic limestone aquifer is considered in this section as an example of a mineralogy in 

which complete dissolution of buffering carbonates due to even long-term ingress of CO2 would 

be implausible. A brief introduction to the slightly impure Dolomitic Limestone assumed below 

is given in Section 3.6.3.4, while Table 4.15 lists its mineralogical composition and provides 

kinetic parameters. The most obvious feature of the Dolomitic Limestone mineralogy is that it 

contains large amounts of calcite (>60% volume fraction) and dolomite (>14% volume fraction), 

both of which strongly buffer pH. 

An equilibrium run provides the initial chemical composition in equilibrium with Dolomitic 

Limestone, prior to intrusion of CO2 (Table 4.16). Compared to the initial chemical composition 

for the Coastal Plain Sandstone, the Dolomitic Limestone aquifer has a slightly higher initial pH 

and a lower Eh. Identical initial Pb and slightly lower As concentrations are obtained compared 

with those of the Coastal Plain Sandstone aquifer.

Figure 4.64 shows profiles of simulated pH for the Dolomitic Limestone at different times during 

ingress of CO2 (left figure) and compares the 100-year profile to the Coastal Plain Sandstone 

aquifer from the base model (right figure). Both mineralogies have an almost identical pH 

profile, which indicates that: (1) the slight difference in initial pH of the groundwater does not 

affect the pH after CO2 intrusion; and (2) the actual volume fraction of buffering minerals is not 

relevant so long as the minerals are present. However, because the Dolomitic Limestone aquifer 

contains much more calcite and dolomite than the Coastal Plain Sandstone, the relative volume 

changes caused by dissolution of these minerals are different. For example, only 1% of the 

calcite fraction dissolves in the sensitivity case during 100 years of CO2 intrusion, compared to 

almost 100% in the Coastal Plain aquifer (Figure 4.65). It can be anticipated that the Coastal 

Plain Sandstone would be depleted of calcite shortly after 100 years, whereas the Dolomitic 

Limestone would buffer pH for thousands of years. 
 

272 of 352

Table 4.15. Mineral volume fractions, possible secondary minerals, and kinetic properties for a Dolomitic 

Limestone 

Parameters for Kinetic Rate Law 

Neutral 

Mechanism 

Acid Mechanism Base Mechanism 

Mineral Volume 

Fraction 

of solid 

 

A 

(cm2

/g) 

k25 

(mol/m2

/s) 

Ea

(KJ 

/mol) 

k25 Ea n(H+) k25 Ea n(H+) 

Primary: 

Quartz 0.05144173 9.8 1.023×10-14 87.7 

K-feldspar 0.00000000 9.8 3.890×10-13 38 8.710×10-11 51.7 0.5 6.310×10-12 94.1 -0.823 

Oligoclase 0.00000000 9.8 1.445×10-12 69.8 2.138×10-10 65 0.457 

Kaolinite 0.05251716 1.95×105 6.918×10-14 22.2 4.898×10-12 65.9 0.777 8.913×10-18 17.9 -0.472 

Smectite-ca 0.01976175 5.64×105 1.660×10-13 35 1.047×10-11 23.6 0.34 3.020×10-17 58.9 -0.4 

Illite 0.0690266 6.68×105 1.660×10-13 35 1.047×10-11 23.6 0.34 3.020×10-17 58.9 -0.4 

Chlorite 0.01805338 9.8 3.02×10-13 88 7.762×10-12 88 0.5 

Kerogen-os 0.02516736 9.8 3.02×10-13 88 7.762×10-12 88 0.5 

Pyrite 0.00271834 12.9 2.52×10-12 62.76 2.34×10-7 43.54 1 

dolomite 0.14265542 12.9 2.52×10-12 62.76 2.34×10-7 43.54 1 

Calcite 0.60326492 Assumed at equilibrium 

Goethite 0.01276885 1.47×105 2.52×10-12 62.76 2.34×10-7 43.54 1 

Apatite 0.00233047 12.9 2.34×10-7 43.54 1 

Barite 0.00017227 12.9 2.34×10-7 43.54 1 

Arsenopyrite 9.5542E-06 12.9 2.34×10-7 43.54 1 

Greenockite 3.2022E-06 12.9 2.34×10-7 43.54 1 

Cinnabar 1.5439E-08 12.9 2.34×10-7 43.54 1 

Galena 8.2817E-06 12.9 2.34×10-7 43.54 1 

Gudmundite 4.1038E-07 12.9 2.34×10-7 43.54 1 

Tiemannite 1.2941E-07 12.9 2.34×10-7 43.54 1 

Uraninite 8.4531E-07 12.9 2.34×10-7 43.54 1 

Sphalerite 9.9324E-05 12.9 2.34×10-7 43.54 1 

Secondary: 

Dolomite 12.9 2.52×10-12 62.76 2.34×10-7 43.54 1 

Magnesite 9.8 4.571×10-10 23.5 4.169×10-7 14.4 1 

Ankerite 9.8 1.260×10-9 62.76 6.457×10-4 36.1 0.5 

Dawsonite 9.8 1.260×10-9 62.76 6.457×10-4 36.1 0.5 

Na-smectite 151.6 1.660×10-13 35 1.047×10-11 23.6 0.34 3.020×10-17 58.9 -0.4 

Pyromorphite 12.9 2.34×10-7 43.54 1 

Ferroselite 12.9 2.34×10-7 43.54 1 

Clausthalite 12.9 2.34×10-7 43.54 1 
 

273 of 352

Table 4.16. Initial chemical composition (mol/L) of groundwater in equilibrium with Coastal Plain Sandstone and 

Dolomitic Limestone. 

Species Coastal Plain Sandstone Dolomitic Limestone 

Eh -0.215 (volt) -0.22 (volt) 

pH 7.61 7.68 

Ca 9.00E-04 6.61E-04 

Mg 2.17E-05 1.69E-05 

Na 1.97E-03 1.97E-03 

K 2.68E-04 2.39E-04 

Fe 5.63E-06 4.86E-06 

Si 9.31E-04 9.72E-04 

TIC 3.33E-03 3.85E-03 

SO4

-2 1.95E-04 1.95E-04 

HS- 1.44E-08 1.45E-08 

Al 4.19E-11 4.23E-11 

Cl 2.07E-04 2.07E-04 

P 1.78E-06 1.88E-06 

Pb 1.30E-09 1.30E-09 

As 4.42E-08 4.32E-08 

Ba 1.12E-06 5.11E-07 

Cd 1.76E-09 1.95E-09 

Hg 2.53E-09 2.57E-09 

Sb 3.57E-10 3.27E-10 

Se 1.69E-08 1.41E-08 

U 4.48E-09 5.28E-09 

Zn 3.40E-07 3.31E-07 

With the pH differences almost negligible, the effects of galena precipitation or arsenopyrite 

dissolution should be fairly similar between the two sensitivity cases. However, the resulting 

concentrations of Pb and As do show small but noticeable differences. The reason is that the 
 

274 of 352

adsorption/desorption characteristics differ because the content of illite and smectite differs 

between the Dolomitic Limestone and the Coastal Plain Sandstone mineralogies. 

4.66 shows the spatial distribution of total aqueous Pb concentrations at different times for 

Dolomitic Limestone (left) and compares them with those for the Coastal Plain Sandstone 

(right). The sensitivity case has a slightly higher Pb concentration in the two-phase region and 

marginally lower Pb concentration downstream. The weak site on illite is the principal surface 

complex that releases Pb, while the strong site on smectite is the principal surface complex that 

adsorbs Pb. Desorption is stronger in the two-phase zone for the Dolomitic Limestone than for 

the Coastal Plain Sandstone (Figure 4.67 and Figure 4.68 left), because the former contains more 

illite than the latter. The Dolomitic Limestone also contains more smectite, which explains why 

more Pb sorbs back onto the strong site on smectite in the downstream region (Figure 4.68 right). 

Overall, the sensitivity run shows a very small amount of Pb in the downstream region, whereas 

the base model exhibits a minor amount of desorption. While hard to observe in Figure 4.68 

(right), the relative changed of total Pb sorbed is +0.15% for the sensitivity case compared to -

0.2% for the base model. 

Figure 4.69 shows the spatial distribution of total aqueous As concentrations at different times 

for the Dolomitic Limestone (left) and a comparison of the 100-year results with those for the 

Coastal Plain Sandstone (right). The sensitivity case shows higher aqueous As concentration than 

the base model. This result is somewhat counterintuitive when considering the large amounts of 

pH buffering minerals present in the Dolomitic Limestone. However, as pointed out before, the 

pH evolution after ingress of CO2 is largely identical for both mineralogies, because the actual 

volume fraction of buffering minerals is not relevant as long as these minerals remain present. 

The difference in the As concentrations is rather caused by the larger illite content in the 

Dolomitic Limestone, which leads to stronger desorption in the two-phase zone and subsequent 

transport of elevated aqueous As in downstream direction. 
 

275 of 352

0 100 200 300 400 500

Distance (m)

5.5

6

6.5

7

7.5

8

pH

5 yr

20 yr

50 yr

100 yr

0 100 200 300 400 500

Distance (m)

5.5

6

6.5

7

7.5

8

pH

100 yr Coastal Plain Sandstone

100 yr Dolomitic Limestone

Fig. 4.64. pH profiles (left) along x-axis at y = 0 m at different times for Dolomitic Limestone, and comparison 

of computed pH with Coastal Plain Sandstone (right). 

0 100 200 300 400 500

Distance (m)

-100

-80

-60

-40

-20

0

20

Calcite volume fraction change (%)

100 yr Coastal Plain Sandstone

100 yr Dolomitic Limestone

Fig. 4.65. Comparison of calcite volume fraction change for Coastal Plain Sandstone and Dolomitic Limestone. 

0 100 200 300 400 500

Distance (m)

0x100

2x10-8

4x10-8

6x10-8

8x10-8

Total aqueous Pb concentration (mol/L)

5 yr

20 yr

50 yr

100 yr

MCL

0 100 200 300 400 500

Distance (m)

0x100

2x10-8

4x10-8

6x10-8

8x10-8

Total aqueous Pb concentration (mol/L)

100 yr Coastal Plain Sandstone

100 yr Dolomitic Limestone

MCL

Fig. 4.66. Total aqueous Pb concentration profiles (left) along x-axis at y = 0 m at different times for Dolomite 

Limestone, and comparison of computed Pb concentration with Coastal Plain Sandstone (right) 
 

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0 100 200 300 400 500

Distance (m)

0.0x100

2.0x10-6

4.0x10-6

6.0x10-6

8.0x10-6

10-5

1.2x10-5

Total Pb sorption sites (mol/L)

100 yr Coastal Plain Sandstone

100 yr Dolomitic Limestone

0 100 200 300 400 500

Distance (m)

-8.0x101

-6.0x101

-4.0x101

-2.0x101

0.0x100

2.0x101

Total Pb sorption sites change (%)

100 yr Coastal Plain Sandstone

100 yr Dolomitic Limestone

Fig. 4.67. Comparison of total Pb concentration on adsorption sites (left) and change (right) for Coastal Plain 

Sandstone and Dolomitic Limestone. 

0 100 200 300 400 500

Distance (m)

0.0x100

2.0x10-6

4.0x10-6

6.0x10-6

8.0x10-6

10-5

1.2x10-5

illw_OPb+ (mol/L)

100 yr Coastal Plain Sandstone

100 yr Dolomitic Limestone

0 100 200 300 400 500

Distance (m)

0.0x100

2.0x10-7

4.0x10-7

6.0x10-7

8.0x10-7

10-6

smes_OPb+ (mol/L)

100 yr Coastal Plain Sandstone

100 yr Dolomitic Limestone

Fig. 4.68. Comparison of surface complex illw_OPb+ (left) and smes_oPb+for Coastal Plain Sandstone and 

Dolomitic Limestone. 

0 100 200 300 400 500

Distance (m)

4.0x10-8

8.0x10-8

1.2x10-7

1.6x10-7

Total aqueous As concentration (mol/L)

5 yr

20 yr

50 yr

100 yr

MCL

0 100 200 300 400 500

Distance (m)

4x10-8

8x10-8

1x10-7

2x10-7

Total aqueous 

As concentration (mol/L)

100 yr Coastal Plain Sandstone

100 yr Dolomitic Limestone

MCL

Fig. 4.69. Total aqueous As concentration profiles (left) along x-axis at y = 0 m at different times for Dolomitic 

Limestone, and comparison of computed As concentration with Coastal Plain Sandstone (right) 
 

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4.8 Oxidizing Conditions 

Section 3.6.3.2 introduces the mineralogy of a Coastal Plain Sandstone under oxidizing 

conditions. Oxidizing conditions may result from continuous recharge with oxygenated water in 

unconfined aquifers, or in response to anthropogenic extraction of potable water. In this section, 

we investigate the behavior of an oxidized aquifer affected by CO2 intrusion. Notice, however, 

that oxygenation itself can lead to the mobilization of heavy metals as a result of oxidative 

dissolution, an effect that is often more relevant than the pH decrease caused by CO2 intrusion. 

As discussed in Section 3.6.3.2, the mineralogical composition of an oxidized aquifer differs 

from an aquifer under reducing conditions. The most significant differences with respect to the 

Coastal Plain Sandstone are that (1) pyrite is oxidized and that (2) cerussite instead of galena 

would be the mineral controlling Pb concentration. Table 4.17 lists the modified mineralogical 

composition of a representative Coastal Plain Sandstone under oxidizing conditions. All minerals 

likely to buffer the redox state under reducing conditions have been eliminated, and only 

cerussite is present as a host mineral for Pb. We focus in this sensitivity case only on the 

potential for Pb contamination; the behavior of As under analogous conditions may be studied in 

a future analysis. 

An equilibrium run reveals the initial chemical composition in equilibrium with the oxidized 

Coastal Plain Sandstone, before the intrusion of CO2. Well-oxidized soils have redox potentials 

of +0.4 to +0.8 V, while extremely reduced soils may have potentials of -0.1 to -0.5 V (Dragun, 

1988). In the equilibrium run, Eh is specified by the redox couple of O2/H2O, giving an Eh value 

of +0.37 V. An Eh value of +0.37 V represents an end case of groundwater redox condition, 

which probably only exists near the recharge area of an aquifer where the water table is direct 

contact with air. The objective of conducting a simulation with Eh of +0.37 V is to establish the 

maximum effect of redox potential on the mobilization of Pb. Table 4.18 compares the chemical 

composition of groundwater for the Coastal Plain Sandstone aquifer under both reducing and 

oxidizing conditions. The latter composition is characterized by vanishingly small concentrations 

for Fe(II) and HS-

; pH is similar. The most relevant difference, of course, is the initial aqueous 
 

278 of 352

Pb concentration in the oxidized aquifer, which is more than three orders of magnitude larger 

than under reducing conditions. In fact, the initial Pb concentration of 1.36× 10-6 mol/L in the 

oxidized aquifer is significantly above the MCL of 7.24× 10-8 mol/L (or 15 ppb). This difference 

is due to cerussite, rather than galena, controlling the aqueous Pb concentration under oxidizing 

conditions (see Section 3.4.6). Note that the following reaction defines cerussite solubility in the 

presence of CO2. According to the PHREEQC (Parkhurst and Appelo, 1999) database, one has: 

2 2

3 3 PbCO S Pb CO K ( ) log 13.13 + − U + =− (4.8) 

Table 4.17. Mineral volume fractions, possible secondary minerals, and kinetic properties for an oxidized Coastal 

Plain Sandstone aquifer. 

Parameters for kinetic rate law 

Neutral mechanism Acid mechanism Base mechanism 

Mineral Volume 

fraction 

of solid 

 

A 

(cm2

/g) 

k25 

(mol/m2

/s) 

Ea

(KJ 

/mol) 

k25 Ea n(H+) k25 Ea n(H+) 

Primary: 

Quartz 0.796 9.8 1.023×10-14 87.7 

K-feldspar 0.0634 9.8 3.890×10-13 38 8.710×10-11 51.7 0.5 6.310×10-12 94.1 -0.823 

Oligoclase 0.0614 9.8 1.445×10-12 69.8 2.138×10-10 65 0.457 

Kaolinite 0.0174 151.6 6.918×10-14 22.2 4.898×10 -12 65.9 0.777 8.913×10-18 17.9 -0.472 

Ca-smectite 0.0139 151.6 1.660×10-13 35 1.047×10-11 23.6 0.34 3.020×10-17 58.9 -0.4 

Illite 0.029 151.6 1.660×10-13 35 1.047×10-11 23.6 0.34 3.020×10-17 58.9 -0.4 

Calcite 0.0097 Assumed at equilibrium 

Goethite 0.00616 12.9 2.52×10-12 62.76 2.34×10-7 43.54 1 

Apatite 2.2×10-4 12.9 2.52×10-12 62.76 2.34×10-7 43.54 1 

Barite 4.9×10-4 

Cerussite 7.99×10-6 12.9 2.52×10-12 62.76 2.34×10-7 43.54 1 

Secondary: 

Magnesite 9.8 4.571×10-10 23.5 4.169×10-7 14.4 1 

Ankerite 9.8 1.260×10-9 62.76 6.457×10-4 36.1 0.5 

Dawsonite 9.8 1.260×10-9 62.76 6.457×10-4 36.1 0.5 

Pyromorphite 12.9 2.52×10-12 62.76 2.34×10-7 43.54 1 

Na-smectite 151.6 1.660×10-13 35 1.047×10-11 23.6 0.34 3.020×10-17 58.9 -0.4 
 

279 of 352

Table 4.18. Chemical composition (mol/L) of groundwater in equilibrium with Coastal Plain Sandstone aquifer 

under reducing and oxidizing and conditions. 

Species Reducing Conditions 

(Base Model) Oxidizing Conditions 

Eh -0.215 (volt) 0.37 (volt) 

pH 7.61 7.78 

Ca 9.00E-04 1.33E-03 

Mg 2.17E-05 1.52E-14 

Na 1.97E-03 1.35E-02 

K 2.68E-04 1.27E-05 

Fe 5.63E-06 6.50E-17 

Si 9.31E-04 1.02E-03 

TIC 3.33E-03 2.27E-03 

SO4

-2 1.95E-04 6.65E-03 

HS- 1.44E-08 2.18E-16 

Al 4.19E-11 1.08E-09 

Cl 2.07E-04 2.08E-05 

P 1.78E-06 1.52E-06 

Pb 1.30E-09 1.36E-06 

Except for the difference in mineralogy and initial chemical conditions, the simulation of CO2

intrusion into the oxidized Coastal Plain Sandstone was conducted for a model setup similar to 

the base model (Section 4.2). Although oxidizing conditions would typically exist in very 

shallow aquifers, we did not change the aquifer depth. 

Figure 4.70 shows the evolution of pH along the x-axis at y=0, which is almost identical with the 

base model under reducing conditions. The pH decrease in the two-phase zone near the intrusion 

location induces the release of Pb from adsorption sites. Figure 4.71, for example, shows a 

decrease in the concentration of surface complexes on illite in the two-phase zone for both the 

strong (left) and weak sites (right). Within 100 years of CO2 intrusion, the total mass of Pb on 

adsorption sites drops by about 6% in the two-phase zone (Figure 4.72 left). The elevated 

aqueous Pb concentrations, mostly induced by desorption in the two-phase zone, migrate 
 

280 of 352

downstream with the groundwater flow, where a small amount of Pb adsorbs onto surface 

complexes (note the minor increase in adsorbed mass downstream of the intrusion zone in Figure 

4.72 left). Note that the solution becomes oversaturated with respect to cerussite in both the twophase zone and downstream of it, which causes Pb to precipitate out as cerussite (note the few 

percent increase in the cerussite volume fraction in Figure 4.72, right). Thus, while cerussite 

controls the initial equilibrium concentrations of Pb within the groundwater and on adsorption 

sites, the increase in aqueous Pb in response to CO2 intrusion is not actually caused by cerussite 

dissolution, but rather by desorption of initially sorbed matter. This could be different if 

adsorption processes were less relevant in the aquifer, e.g., if less clay minerals were present or 

the site density was lower. 

Figure 4.73 shows the resulting evolution of aqueous Pb concentrations along the x-axis. The 

peak in the two-phase zone observed at 5 years is caused by the strong desorption occurring with 

the change in pH. Because surface complexation is a relatively fast process, considered as an 

equilibrium reaction in the current simulations, desorption ceases when the pH in two-phase zone 

stops decreasing. After 100 years, the downstream Pb concentrations have attained a fairly 

uniform concentration, which is about 30 times larger than the initial Pb concentration. Both 

initial and final conditions have Pb concentrations significantly larger than the MCL of 7.24× 10-

8

 mol/L (15 ppb). 

0 100 200 300 400 500

Distance (m)

5.5

6

6.5

7

7.5

8

pH

5 yr

20 yr

50 yr

100 yr

Fig. 4.70. pH profiles along x-axis at y = 0 m, for oxidized Coastal Plain Sandstone. 
 

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0 100 200 300 400 500

Distance (m)

3.50x10-2

3.55x10-2

3.60x10-2

3.65x10-2

3.70x10-2

3.75x10-2

ills

_

OPb+ (mol/L)

5 yr

20 yr

50 yr

100 yr

0 100 200 300 400 500

Distance (m)

2.14x10-2

2.16x10-2

2.18x10-2

2.20x10-2

2.22x10-2

illw_OPb+ (mol/L)

5 yr

20 yr

50 yr

100 yr

Fig. 4.71. Surface complex of Pb on the strong (left) and weak site (right) of illite along x-axis at y = 0 m for 

oxidized Coastal Plain Sandstone. 

0 100 200 300 400 500

Distance (m)

-8

-6

-4

-2

0

2

Total Pb surface complex change (%)

100 yr

0 100 200 300 400 500

Distance (m)

0

2

4

6

Cerussite volume fraction change (%)

5 yr

20 yr

50 yr

100 yr

Fig. 4.72. Relative change of total Pb concentration on adsorption sites (left) and cerussite volume fraction 

change (right) profiles, along x-axis at y = 0 m (left) for oxidized Coastal Plain Sandstone. 

0 100 200 300 400 500

Distance (m)

0x100

4x10-5

8x10-5

1x10-4

2x10-4

Total aqueous Pb concentration (mol/L)

5 yr

20 yr

50 yr

100 yr

Fig. 4.73. Total aqueous Pb concentration profiles (left) along x-axis at y = 0 m at different times for oxidized C 
 

282 of 352

4.9 Three-Dimensional (3D) Simulation of CO2 Intrusion 

A two-dimensional depth-averaged model is used in all simulations described in the previous 

sections. Thus, the vertical variation in gaseous and aqueous CO2, as well as the vertical 

variation in the resulting geochemical changes, has been neglected so far. This raises questions 

regarding the limitations of the 2D assumption. For example, if a 3D model with vertical 

variation were used, how different would be the spatial distribution and magnitude of CO2 in the 

gas phase and dissolved in the aqueous phase? How would these differences affect the pH as 

well as the aqueous concentrations of Pb and As? Would the observed discrepancies between 2D 

and 3D modeling be less relevant for the base model described in Section 4.5, where most of the 

gaseous CO2 readily dissolves in water, compared to the sensitivity cases described in Sections 

4.6.8 and 4.6.9, where much larger CO2 gas plumes are observed suggesting that buoyancy is 

more relevant? 

To answer these questions, a three-dimensional model was developed using the same model 

setup and similar model dimensions as in the 2D simulations. Like the 2D model, the 3D domain 

is 500 m long and has a uniform thickness of 10 m. However, the vertical extent of the model is 

now discretized using five model layers of 2 m thickness each. To increase computational 

efficiency, the lateral extent of the 3D model is reduced to 100 m, which was shown to be 

sufficient in the 2D simulations. CO2 enters the model domain from below. Three simulation 

cases are considered: The first one uses a model setup identical to the 2D base model described 

in Section 4.5, except that vertical variability is now considered (Section 4.9.1). The second case 

is identical to the 2D sensitivity case described in Section 4.6.8, featuring a reduced groundwater 

flow velocity (Section 4.9.2). The third case is identical to the 2D case described in Section 

4.6.9, where a higher CO2 leakage rate is assumed (Section 4.9.3). Note that in all cases, a 

confined aquifer has been assumed, so that gaseous CO2 cannot escape into the overlying vadose 

zone. This is a conservative assumption because excess CO2 (i.e., gaseous and buoyant CO2 not 

dissolving into the groundwater), which would otherwise escape into the vadose zone, is forced 

to remain in the aquifer. 
 

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4.9.1 Results from 3D Simulation of Base Model 

The CO2 leakage rate assumed in the base-case simulation is 7.5×10-5 kg/s, which is about 2.4 

tonnes per year. As pointed out in Section 4.2, this leakage rate was chosen roughly based on the 

amount of CO2 that can dissolve into the groundwater at a flow velocity of 10 m per year. Thus, 

buoyancy effects should be less relevant in this case, and the 2D model simplification should be 

a reasonable surrogate for the more complex 3D simulations. We will demonstrate this in the 

following discussion of 3D simulation results, starting with a set of figures with a close-in 

viewing of the intrusion zone and immediate vicinity. 

Figures 4.74 and 4.75 show CO2 gas saturation after 100 years of leakage, both in a 3D contour 

plot and in several y-z cross-sections. Consistent with the 2D results described in Section 4.5.1, 

the two-phase zone predicted by the 3D model is relatively small, extending about 30 m in the 

direction of groundwater flow (x-direction) and about 10 m perpendicular to it (y-direction). 

Vertically, the two-phase zone extends almost to the top of the aquifer, but does not accumulate 

there, as most of the gaseous CO2 effectively dissolves into the groundwater. Plots of dissolved 

CO2 (which is given by the concentration of total inorganic carbonate (TIC)) are shown in 

Figures 4.76 and 4.77. Qualitatively, the 3D results are again similar to those obtained from the 

2D model. An elevated concentration of TIC is observed in the two-phase region near the 

intrusion location and further downstream, resulting from slow advective transport with the 

groundwater, while lateral spreading of dissolved CO2 is minor. TIC contours indicate a fairly 

uniform distribution of dissolved CO2 with depth. 

While qualitatively similar, there are some differences between 2D and 3D results. For example, 

the peak CO2 gas saturation and the peak concentrations of dissolved CO2 as predicted from the 

3D model, observed near the intrusion point at the base of the aquifer, are higher than those in 

the 2D model. As a result, the lowest pH value calculated in the 3D model is around 5.5 (see 

Figure 4.78 and 4.79), which is more acidic than in the 2D model (pH is equal to 5.65). 

However, outside of the immediate intrusion location, the predicted pH changes from CO2

intrusion are in fair agreement between the 3D and 2D models. Reasonable agreement between 
 

284 of 352

3D and 2D can also be seen in the predicted Pb and As concentrations, which correlate well with 

the groundwater volume experiencing pH changes (Figure 4.80 to 4.83). Elevated Pb and As 

have peak values near the intrusion location and slightly smaller values in the downstream 

direction. Except for the uppermost portion of the aquifer, aqueous Pb and As are well 

equilibrated over the depth of the aquifer. 

Figures 4.84 through 4.87 provide, respectively, contours of TIC, pH, Pb, and As from the 3D 

model, showing the entire length (in the x-direction) of the model domain rather than focusing on 

the intrusion location. These figures reiterate qualitatively what was observed before in the 2D 

results. Transport in the longitudinal direction is dominant, with little lateral spreading. They also 

show that, while there is some vertical variability further downstream, the observed geochemical 

behavior is relatively uniform with depth. 

A direct and quantitative comparison of predicted 3D and 2D results is provided in Figures 4.88 

and 4.89. Figure 4.88 shows total aqueous Pb concentration along the x-axis at y = 0 m for 

different aquifer depths (top at z = 0 m, middle at z = -5 m, and bottom at z = -10 m) in the 3D 

model, together with the depth-averaged results from the 2D model. The highest Pb 

concentrations are observed at the bottom and in the middle of the aquifer, much higher than 

those observed at the top. As discussed above, this is because gaseous CO2 dissolves before 

reaching the aquifer top. The 2D concentration profiles resemble those of the 3D simulations, but 

deviate from them by about 20-30% when comparing the aquifer bottom and top. Figure 4.89 

shows the comparison of As concentrations for 3D and 2D models. Results from the depthaveraged 2D model are very similar to those predicted at the bottom and in the middle of the 3D 

domain. 
 

285 of 352

-5 Z

90

100

110

120

130

X

0

10

20

30

40

Y

Y

X

Z

SG

0.091

0.085

0.079

0.073

0.067

0.061

0.055

0.048

0.042

0.036

0.030

0.024

0.018

0.012

0.006

Fig. 4.74. Close-up view of spatial distribution of CO2 gas saturation after 100 years of CO2 intrusion in 3D 

model with CO2 leakage rate of 7.5×10-5 kg/s. 

Fig. 4.75. Profiles CO2 gas saturation at x= 95 m, x =105 m, x = 115 m, and x = 125 m after 100 years of CO2

intrusion in the 3D model with CO2 leakage rate of 7.5×10-5 kg/s. 
 

286 of 352

-5 Z

90

100

110

120

130

X

0

10

20

30

40

Y

Y

X

Z

TIC

0.22

0.20

0.19

0.17

0.16

0.14

0.13

0.12

0.10

0.09

0.07

0.06

0.04

0.03

0.02

Fig. 4.76. Close-up view of spatial distribution of Total Inorganic Carbon (TIC) after 100 years of CO2 intrusion 

in 3D model with CO2 leakage rate of 7.5×10-5 kg/s. 

Fig. 4.77. Profiles of concentration of Total Inorganic Carbon (TIC) at x= 95 m, x =105 m, x = 115 m, and x = 

125 m after 100 years of CO2 intrusion in the 3D model with CO2 leakage rate of 7.5×10-5 kg/s. 
 

287 of 352

-5 Z

90

100

110

120

130

X

0

10

20

30

40

Y

Y

Z

X

pH

7.48

7.35

7.22

7.09

6.96

6.83

6.70

6.57

6.44

6.31

6.18

6.05

5.92

5.79

5.66

Fig. 4.78. Close-up view of spatial distribution of pH in 3D model after 100 years of CO2 intrusion in the 3D 

model with CO2 leakage rate of 7.5×10-5 kg/s. 

Fig. 4.79. Profiles of pH at x= 95 m, x =105 m, x = 115 m, and x = 125 m after 100 years of CO2 intrusion in the 

3D model with CO2 leakage rate of 7.5×10-5 kg/s. 
 

288 of 352

-5 Z

90

100

110

120

130

X

0

10

20

30

40

Y

Y

Z

X

Pb

2.7E-08

2.5E-08

2.3E-08

2.2E-08

2.0E-08

1.8E-08

1.6E-08

1.5E-08

1.3E-08

1.1E-08

9.4E-09

7.6E-09

5.9E-09

4.2E-09

2.4E-09

Fig. 4.80. Close-up view of spatial distribution of total aqueous Pb concentration after 100 years of CO2 intrusion 

in the 3D model with CO2 leakage rate of 7.5×10-5 kg/s. 

Fig. 4.81. Profiles of total aqueous Pb concentration at x= 95 m, x =105 m, x = 115 m, and x = 125 m after 100 

years of CO2 intrusion in the 3D model with CO2 leakage rate of 7.5×10-5 kg/s. 
 

289 of 352

-5 Z

90

100

110

120

130

X

0

10

20

30

40

Y

Y

X

Z

As

1.4E-07

1.3E-07

1.2E-07

1.2E-07

1.1E-07

1.1E-07

9.9E-08

9.3E-08

8.7E-08

8.1E-08

7.5E-08

6.9E-08

6.3E-08

5.6E-08

5.0E-08

Fig. 4.82. Close-up view of spatial distribution of total aqueous As concentration after 100 years of CO2 intrusion 

in the 3D model with CO2 leakage rate of 7.5×10-5 kg/s. 

Fig. 4.83. Profiles of total aqueous As concentration at x= 95 m, x =105 m, x = 115 m, and x = 125 m after 100 

years of CO2 intrusion in the 3D model with CO2 leakage rate of 7.5×10-5 kg/s. 
 

290 of 352

-5

Z

0

100

200

300

400

X

50Y

Y

Z

X

TIC

0.22

0.20

0.19

0.17

0.16

0.14

0.13

0.12

0.10

0.09

0.07

0.06

0.04

0.03

0.02

Fig. 4.84. Spatial distribution of Total Inorganic Carbon (TIC) after 100 years of CO2 intrusion in 3D model with 

CO2 leakage rate of 7.5×10-5 kg/s. 

-5 Z

0

100

200

300

400

X

50 Y

Y

Z

X

pH

7.48

7.35

7.22

7.09

6.96

6.83

6.70

6.57

6.44

6.31

6.18

6.05

5.92

5.79

5.66

Fig. 4.85. Spatial distribution of pH after 100 years of CO2 intrusion in 3D model with CO2 leakage rate of 

7.5×10-5 kg/s. 
 

291 of 352

-5 Z

0

100

200

300

400

X

50 Y

Y

Z

X

Pb

2.7E-08

2.5E-08

2.3E-08

2.2E-08

2.0E-08

1.8E-08

1.6E-08

1.5E-08

1.3E-08

1.1E-08

9.3E-09

7.6E-09

5.8E-09

4.1E-09

2.3E-09

Fig. 4.86. Spatial distribution of total aqueous Pb concentration after 100 years of CO2 intrusion in 3D model 

with CO2 leakage rate of 7.5×10-5 kg/s. 

-5 Z

0

100

200

300

400

X

50 Y

Y

Z

X

As

1.4E-07

1.3E-07

1.2E-07

1.2E-07

1.1E-07

1.1E-07

9.9E-08

9.3E-08

8.7E-08

8.1E-08

7.5E-08

6.9E-08

6.3E-08

5.6E-08

5.0E-08

Fig. 4.87. Spatial distribution of total aqueous As concentration after 100 years of CO2 intrusion in 3D model 

with CO2 leakage rate of 7.5×10-5 kg/s. 
 

292 of 352

0 100 200 300 400 500

Distance (m)

0x100

2x10-8

4x10-8

6x10-8

8x10-8

Total aqueous Pb concentration (mol/L)

3D y=0 z=0

3D y=0 z=-5

3D y=0 z=-10

2D

MCL

Fig. 4.88. Total aqueous Pb concentration along x-axis at y = 0 m and different aquifer depths (z = 0, -5 and 

-10 m) for 3D model and total aqueous Pb concentration along x-axis at y = 0 m for 2D model. 

0 100 200 300 400 500

Distance (m)

4.0x10-8

6.0x10-8

8.0x10-8

10-7

1.2x10-7

1.4x10-7

1.6x10-7

Total aqueous As concentration (mol/L)

3D y=0 z=0

3D y=0 z=-5

3D y=0 z=-10

2D

MCL

Fig. 4.89. Total aqueous As concentration along x-axis at y = 0 m and different aquifer depth (z = 0, -5 and 

-10 m) for 3D model and total aqueous As concentration along x-axis at y = 0 m for 2D model. 

We may conclude that the 2D results provide a reasonable approximation of the threedimensional system behavior. The differences observed between 3D and 2D are well within the 

range stemming from uncertainties in many of the geochemical parameters. Of course, this is a 

conclusion specific to the considered case, which has a small zone of gaseous CO2 and is 

therefore not dominated by buoyancy effects. Furthermore, we point out that the 
 

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TOUGHREACT transport model has been set up such that lateral and vertical mixing is limited 

(i.e., by setting hydrodynamic dispersion to zero, or by choosing homogenous rather than 

spatially distributed parameters). More vertical mixing in the 3D model would lead to stronger 

equilibration over the thickness of the aquifer, thus further improving the agreement between 2D 

and 3D models. 

4.9.2 Sensitivity to Groundwater Flow Velocity—3D Model 

Our 2D simulation of the sensitivity to groundwater flow velocity case indicates that a much 

larger two-phase zone with gaseous CO2 and water evolves when the groundwater flow rate 

decreases to 1 m/y (Section 4.6.8). In such a case, buoyancy forces should be more relevant than 

in the base model, suggesting that a 3D model representation may be necessary. Figures 4.90 and 

4.91 show the 3D model results of CO2 gas saturation after 100 years of CO2 intrusion in an 

aquifer with a groundwater flow rate of 1 m/yr. With less water flowing towards the intrusion 

zone, dissolution of CO2 is less effective. As a result, most of the leaking CO2 remains in the gas 

phase, migrates upward until it reaches the upper closed boundary of the confined aquifer, and 

then spreads out laterally. A strongly three-dimensional behavior can be seen, with significant 

vertical variability, which is not captured by the 2D model results shown in Section 4.6.8. 

Figures 4.92 and 4.93 provide contours of dissolved CO2 (given as total inorganic carbon, TIC) 

as predicted by the 3D model. CO2 has dissolved into the aqueous phase wherever gaseous CO2

is in contact with water. Thus the region of elevated TIC corresponds roughly with the CO2

plume volume, with high levels of TIC near the intrusion location and at the aquifer top. 

However, some advective and diffusive transport of dissolved CO2 also widens the region 

somewhat, with elevated TIC observed mainly in the downstream direction from the intrusion 

location. 

The spatial distributions of pH, total aqueous Pb, and total aqueous As concentration shown in 

Figures 4.94 through 4.99 correlate well with the TIC contours. Due to the buoyancy of the CO2

gas, contamination occurs mainly near the top of aquifer. In this region, increased acidity and 
 

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elevated contaminant concentrations extends far from the intrusion location, both downstream 

and upstream as well as laterally. In the lower portions of the aquifer, contamination occurs 

primarily near the location of intrusion. 

A direct and quantitative comparison of predicted 3D and 2D results for this sensitivity case is 

provided in Figures 4.100 and 4.101. Figure 4.100 shows the total aqueous Pb concentration 

along the x axis for different aquifer depths (top at z = 0 m, middle at z = -5 m, and bottom at z = 

-10 m) in the 3D model, together with the depth-averaged results from the 2D model. The top 

figures are for x-axis profiles at y = 0 m; i.e., they intersect the CO2 intrusion location. The 

bottom figures are at y = 50 m, away from the intrusion location. While the predicted maximum 

concentrations are similar, the 2D results clearly underestimate the extent of contamination at the 

top of the aquifer and do not capture the spatial characteristics of contamination in the lower 

aquifer body. Figure 4.101 for As shows similar discrepancies. We conclude that this sensitivity 

case clearly demonstrates the necessity of using a 3D model, especially in aquifers where 

advective velocity is low. The 2D results can, at best, provide a first-order approximation of the 

system behavior. 
 

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-5 Z

0

100

200

300

400

X

50 Y

Y

X

Z

SG

0.21

0.19

0.18

0.17

0.15

0.14

0.12

0.11

0.10

0.08

0.07

0.06

0.04

0.03

0.01

Fig. 4.90. Spatial distribution of CO2 gas saturation after 100 years of CO2 intrusion in 3D model with flow rate 

of 1 m/y. 

Fig. 4.91. Profiles of CO2 gas saturation at different locations after 100 years of CO2 intrusion in 3D model with 

flow rate of 1 m/y. 
 

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-5

Z

0

50

100

150

200

250

300

350

400

X

50 Y

Y

X

Z

TIC

0.21

0.20

0.18

0.17

0.15

0.14

0.13

0.11

0.10

0.09

0.07

0.06

0.04

0.03

0.02

Fig. 4.92. Spatial distribution of total inorganic carbon (TIC) after 100 years of CO2 intrusion in 3D model with 

flow rate of 1 m/y. 

Fig. 4.93. Total inorganic carbon (TIC) y-z profile at different locations after 100 years of CO2 intrusion in 3D 

model with flow rate of 1 m/y. 
 

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-5

Z

0

50

100

150

200

250

300

350

400

X

50 Y

Y

Z

X

pH

7.48

7.35

7.22

7.10

6.97

6.84

6.71

6.58

6.45

6.32

6.20

6.07

5.94

5.81

5.68

Fig. 4.94. Spatial distribution of pH after 100 years of CO2 intrusion in 3D model with flow rate of 1 m/y. 

Fig. 4.95. Profiles of pH at different locations after 100 years of CO2 intrusion in 3D model with flow rate of 1 

m/y. 
 

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-5

Z

0

50

100

150

200

250

300

350

400

X

50 Y

Y

Z

X

Pb

3.6E-08

3.4E-08

3.1E-08

2.9E-08

2.6E-08

2.4E-08

2.2E-08

1.9E-08

1.7E-08

1.5E-08

1.2E-08

1.0E-08

7.7E-09

5.3E-09

3.0E-09

Fig. 4.96. Spatial distribution of total aqueous Pb concentration after 100 years of CO2 intrusion in 3D model 

with flow rate of 1 m/y. 

Fig. 4.97. Profiles of total aqueous Pb concentration at different locations after 100 years of CO2 intrusion in 3D 

model with flow rate of 1 m/y. 
 

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-5

Z

0

50

100

150

200

250

300

350

400

X

50 Y

Y

Z

X

As

1.2E-07

1.1E-07

1.1E-07

1.0E-07

9.8E-08

9.3E-08

8.8E-08

8.3E-08

7.8E-08

7.4E-08

6.9E-08

6.4E-08

5.9E-08

5.4E-08

4.9E-08

Fig. 4.98. Spatial distribution of total aqueous As concentration after 100 years of CO2 intrusion in 3D model 

with flow rate of 1 m/y. 

Fig. 4.99. Profiles of total aqueous As concentration at different locations after 100 years of CO2 intrusion in 3D 

model with flow rate of 1 m/y. 
 

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0 100 200 300 400 500

Distance (m)

0x100

2x10-8

4x10-8

6x10-8

8x10-8

Total aqueous Pb concentration (mol/L)

3D y=0 z=0

3D y=0 z=-5

3D y=0 z=-10

2D y=0

MCL

0 100 200 300 400 500

Distance (m)

0x100

2x10-8

4x10-8

6x10-8

8x10-8

Total aqueous Pb concentration (mol/L)

3D y=50 z=0

3D y=50 z=-5

3D y=50 z=-10

2D y=50

MCL

Fig. 4.100. Total aqueous Pb concentration along x-axis at different aquifer depths (z = 0, -5 and -10 m) for 3D 

model and total aqueous Pb concentration along x-axis for 2D model (both models have a flow rate of 

1 m/y). Top figure shows x-axis profiles at y = 0 m, bottom figure shows profiles y = 50 m. 
 

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0 100 200 300 400 500

Distance (m)

4.0x10-8

6.0x10-8

8.0x10-8

10-7

1.2x10-7

1.4x10-7

Total aqueous As concentration (mol/L)

3D y=0 z=0

3D y=0 z=-5

3D y=0 z=-10

2D y=0

MCL

0 100 200 300 400 500

Distance (m)

4.0x10-8

6.0x10-8

8.0x10-8

10-7

1.2x10-7

1.4x10-7

Total aqueous As concentration (mol/L)

3D y=50 z=0

3D y=50 z=-5

3D y=50 z=-10

2D y=50

MCL

Fig. 4.101. Total aqueous As concentration along x-axis at different aquifer depths (z = 0, -5 and -10 m) for 3D 

model and total aqueous As concentration along x-axis for 2D model (both models have a flow rate of 

1 m/y). Top figure shows x-axis profiles at y = 0 m, bottom figure shows profiles at y = 50 m. 
 

302 of 352

4.9.3 Sensitivity to CO2 Leakage Rate — 3D Model 

An increased CO2 leakage rate (by a factor of 8) has shown that a large CO2 gas plume is 

generated, as evident in the 2D results (Section 4.6.9), suggesting that buoyancy forces are 

relevant and that a 3D model should provide a better simulation. Figures 4.102 and 4.103 show 

model results of CO2 gas saturation, as predicted by the 3D model, for the situation after 100 

years of CO2 leakage into the aquifer. As expected, the buoyancy of CO2 leads to accumulation 

of a gaseous plume at the top of the aquifer and induces significant vertical variation in gas 

saturation. Note that the groundwater flow direction affects the distribution of the CO2 plume at 

the aquifer top, which extends much further downstream than upstream. 

Figures 4.104 and 4.105 show contours of dissolved CO2 (total inorganic carbon). Two principal 

factors affect the spatial distribution of TIC: (1) the distribution of the CO2 plume, and (2) the 

advective-diffusive transport of dissolved CO2 with the groundwater flow. The dissolved CO2

concentrations thus follow the general pattern of the CO2 plume (e.g., the spreading of the plume 

at the top), but also show a significant migration of CO2-charged water from the intrusion 

location further downstream (evident at the bottom of the aquifer along the x-axis). The spatial 

distributions of pH, total aqueous Pb, and total aqueous As concentration (Figure 4.106 to 4.111) 

follow similar trends as TIC. Areas with increased acidity as well as high Pb and As correspond 

to areas with elevated TIC. Overall, a strongly three-dimensional behavior can be seen in these 

plots, with significant vertical variability, which is not well represented by the 2D model results 

shown in Section 4.6.9. 

A direct and quantitative comparison of predicted 3D and 2D results for this sensitivity case is 

provided in Figures 4.112 and 4.113. The figures show, respectively, total aqueous Pb and As 

concentration along the x-axis for different aquifer depths (top at z = 0 m, middle at z = -5 m, 

and bottom at z = -10 m) in the 3D model, together with the depth-averaged results from the 2D 

model. The top figures are for profiles at y = 0 m, intersecting the CO2 intrusion location. The 

bottom figures are for profiles at y = 50 m. Interestingly, the 3D and 2D results are not too 

different for the top figures; there is no significant vertical variation in the 3D results measured 
 

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along the x-axis. One reason is that the groundwater above the intrusion location appears to be 

completely saturated with CO2, meaning that the TIC does not increase further even though 

gaseous CO2 continues to transit upward from the bottom to the top of the aquifer. Carbon 

dioxide is thus relatively well equilibrated in the vertical direction near the intrusion location, 

and vertically uniform geochemical conditions are observed. This uniformity does not deviate 

significantly further downstream, because the CO2-charged and possibly contaminated water 

travels sufficiently far within the 100-year simulation time to reach the downstream model 

boundary. In other words, contamination via transport of dissolved CO2 in the lower portion of 

the aquifer is comparable with contamination where extensive spreading of the gas plume occurs 

at the top of aquifer. However, as Figures 4.106 through 4.111 suggest, vertical variability of 

TIC, pH, Pb, and As is much stronger for all locations offset from that immediately downstream 

of the intrusion location (i.e., away from y = 0 m), indicating that indeed a 3D model is preferred 

for this sensitivity case. This is clearly evident from the x-axis profiles at y = 50 m in Figures 

4.112 and 4.113, where the 2D results are in good agreement with the 3D concentrations at the 

top of the aquifer (where gaseous CO2 accumulates), but strongly overpredict those in the lower 

aquifer portions. 
 

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-5 Z

0

100

200

300

400

X

50 Y

Y

Z

X

SG

0.25

0.23

0.22

0.20

0.18

0.17

0.15

0.13

0.12

0.10

0.08

0.07

0.05

0.03

0.02

Fig. 4.102. Spatial distribution of CO2 gas saturation after 100 years of CO2 intrusion in 3D model with CO2

leakage rate of 6×10-4 kg/s. 

Fig. 4.103. Profiles of CO2 gas saturation at different locations after 100 years of CO2 leakage in the 3D model 

with CO2 intrusion rate of 6×10-4 kg/s. 
 

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-5 Z

0

100

200

300

400

X

50Y

Y

Z

X

TIC

0.22

0.21

0.19

0.18

0.16

0.15

0.13

0.12

0.10

0.09

0.08

0.06

0.05

0.03

0.02

Fig. 4.104. Spatial distribution of TIC after 100 years of CO2 intrusion in 3D model with CO2 leakage rate of 

6×10-4 kg/s. 

Fig. 4.105. Profiles of Total Inorganic Carbon (TIC) at different locations after 100 years of CO2 intrusion in the 

3D model with CO2 leakage rate of 6×10-4 kg/s. 
 

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-5 Z

0

100

200

300

400

X

50Y

Y

Z

X

pH

7.48

7.35

7.22

7.09

6.96

6.83

6.70

6.57

6.44

6.31

6.18

6.05

5.92

5.79

5.66

Fig. 4.106. Spatial distribution of pH after 100 years of CO2 intrusion in 3D model with CO2 leakage rate of 6×10-

4

 kg/s. 

Fig. 4.107. Profiles of pH at different locations after 100 years of CO2 intrusion in the 3D model with CO2 leakage 

rate of 6×10-4 kg/s. 
 

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-5 Z

0

100

200

300

400

X

50Y

Y

Z

X

Pb

3.75E-08

3.50E-08

3.25E-08

3.00E-08

2.75E-08

2.51E-08

2.26E-08

2.01E-08

1.76E-08

1.51E-08

1.26E-08

1.01E-08

7.66E-09

5.17E-09

2.69E-09

Fig. 4.108. Spatial distribution of total aqueous Pb concentration after 100 years of CO2 intrusion in 3D model 

with CO2 leakage rate of 6×10-4 kg/s. 

Fig. 4.109. Profiles of total aqueous Pb concentration at different locations after 100 years of CO2 intrusion in the 

3D model with CO2 leakage rate of 6×10-4 kg/s. 
 

308 of 352

-5 Z

0

100

200

300

400

X

50Y

Y

Z

X

As

1.27E-07

1.22E-07

1.16E-07

1.10E-07

1.05E-07

9.94E-08

9.39E-08

8.84E-08

8.29E-08

7.73E-08

7.18E-08

6.63E-08

6.07E-08

5.52E-08

4.97E-08

Fig. 4.110. Spatial distribution of total aqueous As concentration after 100 years of CO2 intrusion in 3D model 

with CO2 leakage rate of 6×10-4 kg/s. 

Fig. 4.111. Profiles of total aqueous As concentration at different locations after 100 years of CO2 intrusion in the 

3D model with CO2 leakage rate of 6×10-4 kg/s. 
 

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0 100 200 300 400 500

Distance (m)

0x100

2x10-8

4x10-8

6x10-8

8x10-8

Total aqueous Pb concentration (mol/L)

3D y=0 z=0

3D y=0 z=-5

3D y=0 z=-10

2D y=0

MCL

0 100 200 300 400 500

Distance (m)

0x100

2x10-8

4x10-8

6x10-8

8x10-8

Total aqueous Pb concentration (mol/L)

3D y=50 z=0

3D y=50 z=-5

3D y=50 z=-10

2D y=50

MCL

Fig. 4.112. Total aqueous Pb concentration along x-axis at different aquifer depths (z = 0, -5 and -10 m) for 3D 

model and total aqueous Pb concentration along x-axis for 2D model (both models have a CO2

intrusion rate of 6×10-4 kg/s). Top figure shows x-axis profiles at y = 0 m, bottom figure shows profiles 

y = 50 m. 
 

310 of 352

0 100 200 300 400 500

Distance (m)

4.0x10-8

6.0x10-8

8.0x10-8

10-7

1.2x10-7

1.4x10-7

Total aqueous As concentration (mol/L)

3D y=0 z=0

3D y=0 z=-5

3D y=0 z=-10

2D y=0

MCL

0 100 200 300 400 500

Distance (m)

4.0x10-8

6.0x10-8

8.0x10-8

10-7

1.2x10-7

1.4x10-7

Total aqueous As concentration (mol/L)

3D y=50 z=0

3D y=50 z=-5

3D y=50 z=-10

2D y=50

MCL

Fig. 4.113. Total aqueous As concentration along x-axis at different aquifer depths (z = 0, -5 and -10 m) for 3D 

model and total aqueous As concentration along x-axis for 2D model (both models have a CO2

intrusion rate of 6×10-4 kg/s). Top figure shows x-axis profiles at y = 0 m, bottom figure shows profiles 

y = 50 m. 
 

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4.10 Summary of Modeling Results 

Systematic reactive transport simulations have been conducted to evaluate the effect of CO2

intrusion into domestic shallow potable aquifers. A synthetic simplified hydrogeological 

situation was considered as described in Section 4.2. The model starts with the basic assumption 

that CO2 stored in deep geologic units can escape via preferential pathways, such as a fault zone, 

and enter the shallow aquifer locally from below at a constant rate. The reactive transport model 

is used to predict the transport of gaseous and dissolved CO2 within the shallow aquifer as well 

as the geochemical changes introduced by the presence of CO2, in particular the mobilization of 

potentially hazardous constituents and their migration in the groundwater. Various sensitivity 

studies have been conducted for different hydrological, geochemical, and mineralogical 

conditions, to (1) account (partially) for the range of conditions found in domestic potable 

aquifers and their host rocks, and (2) to evaluate the uncertainty associated with geochemical 

processes and model parameters. The base model setup and the sensitivity scenarios have been 

selected based on the geochemical model definition described in Section 3. 

With respect to hazardous constituents, the focus in this modeling study is on Pb and As 

mobilization. It is assumed in all simulation cases that small volume fractions of minerals 

containing hazardous constituents are present in the aquifers, including those containing Pb and 

As as essential components. Based on the discussion in Sections 3.3.3 and 3.6.2.2, the presence 

of such trace minerals can be considered a reasonable possibility in all aquifers. The minerals 

that are considered to control the aqueous concentrations of Pb and As are, respectively, galena 

and arsenopyrite (the latter in solid solution in pyrite). These were identified in Section 3.4 as 

likely solubility-controlling mineral hosts under reducing conditions, a conclusion obtained from 

thermodynamic evaluation of more than 35,000 groundwater analyses in the NWIS database. 

Equilibrium runs were conducted prior to modeling CO2 intrusion to establish the initial 

chemical composition of the groundwater at equilibrium with the selected mineralogy. This 

initiation step also establishes equilibrium between mineral solubilities and adsorption sites, 

essentially “loading” these up. Thus, the selection of solubility-controlling minerals for 
 

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hazardous inorganic constituents not only defines the initial aqueous concentration in the 

groundwater, but also affects the amount of contaminants initially sorbed onto clays or goethite. 

Starting with the equilibrated initial conditions, the impact of CO2 intrusion was then modeled 

with the reactive transport simulator TOUGHREACT, for a simulation time period of 100 years 

with continuous ingress of CO2. Our simulation results suggest that CO2 ingress into a shallow 

aquifer can lead to significant and long-lasting mobilization of Pb and As, contaminating the 

groundwater near the intrusion location and further downstream of it. However, while strong 

increases in aqueous concentrations are predicted to occur compared to the initial water 

composition, the MCL for As in groundwater has been exceeded in only a few simulation cases, 

whereas the MCL for Pb has not been exceeded at all. According to our sensitivity study, many 

conditions and factors impact the severity and spatial distribution of groundwater contamination 

in response to CO2 intrusion. Below, we summarize the important implications from the 

sensitivity study and discuss the relative importance of various conditions and factors. 

Base Model

The base model described in Sections 4.2 and 4.5 assumes CO2 entering a shallow aquifer of 10 

m thickness at an annual rate of about 2.4 tonnes. The aquifer is in a reducing environment. The 

host rock mineralogy, referred to as Coastal Plain Sandstone, is representative of an impure 

sandstone aquifer. According to the model predictions, the local ingress of CO2 results in a small 

two-phase zone and an elongated elevated dissolved CO2 zone along the flow direction. This 

leads to moderate increases in acidity within the two-phase zone as well as in the downstream 

area, followed by mobilization of Pb and As. The mobilization of Pb and As depends on the 

complex transient interaction between adsorption/desorption onto/from mineral surfaces and 

dissolution/ precipitation of relevant minerals. 

In the case of Pb, desorption of initially sorbed mass is the main contributor to the observed 

increase in aqueous concentrations. In fact, desorption releases enough Pb from adsorption sites 

that the solution becomes supersaturated with respect to galena, and precipitation of this mineral 

occurs. The resulting aqueous concentrations of Pb are about one order of magnitude higher than 
 

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the initial concentrations, but lower than the MCL (less than 3 ppb compared to the MCL of 15 

ppb) within the small two-phase zone and the elongated downstream plume. As pointed out in 

Section 3.5, sulfide and selenide aqueous complexation play a very important role in the 

behavior and evolution of Pb in response to CO2 intrusion. Because very stable neutral 

complexes form in solution, the impact that elevated CO2 levels have on increasing the solubility 

of galena (PbS), for example, is reduced compared to the case where no complexation is 

considered. While not reported here, we have conducted reactive transport simulations without 

accounting for sulfide and selenide complexes in solution. These resulted in higher overall 

aqueous Pb concentrations after CO2 intrusion compared to the base model, because both 

desorption and galena dissolution caused Pb mobilization in the two-phase zone. 

Contrary to what is observed for Pb, desorption processes do not release sufficient As into 

solution to inhibit the dissolution of arsenopyrite. Therefore, desorption of As from mineral 

surface and dissolution of arsenopyrite occur simultaneously and lead to a substantial increase of 

aqueous As concentrations. The maximum concentration almost reaches the MCL (10 ppb) 

within the small two-phase zone and is slightly lower (about 9 ppb) than the MCL within the 

elongated area of increased acidity downstream of the intrusion location. 

The mobilization of Pb and As, from minerals and from adsorption sites, causes only very small 

changes to the initial contaminant mass in the solid phase, even after a time period of 100 years 

of CO2 intrusion. For example, although the initial volume fraction of arsenopyrite in the aquifer 

is very small, on the order of 10-5 of the total mineral volume, this mineral is far from being 

depleted after 100 years of increased acidity. Very small amounts of Pb and As contained in the 

aquifer solid phase can provide a long-lasting source of contamination. 

Importance of Adsorption/Desorption Processes

Our simulations indicate that adsorption/desorption via surface complexation is arguably the 

most important process controlling the fate of hazardous constituents mobilized by CO2 leakage 

when sorbing minerals are present. Adsorbed species are modeled in the TOUGHREACT 

simulations via a surface complexation model that takes into account the competitive 
 

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complexation of As and Pb on several clays and goethite. Our simulation results suggest that 

desorption of initially sorbed As and dissolution of arsenopyrite act together in mobilizing As 

near the region of CO2 intrusion, whereas the desorption of Pb from mineral surfaces is so 

significant that galena precipitates instead of dissolving. The opposite behavior is seen further 

downstream. There, adsorption removes significant amounts of Pb and As from the solution, 

dampening further increases of contaminant concentrations. 

The importance of adsorption is clearly demonstrated in Section 4.6.1.1, where a simulation 

without adsorption is compared to the base model considering adsorption. Without adsorption, 

the resulting long-term contamination of an aquifer would be less significant near the region 

where CO2 enters, whereas aqueous concentrations would keep increasing with travel distance 

(eventually exceeding MCLs), as acidic water migrates further and more dissolution occurs. 

Thus, without adsorption, an important process mitigating the long-term impact of CO2 intrusion 

would be neglected. Furthermore, whereas adsorption/desorption reactions are always assumed 

to equilibrate instantaneously, the dissolution or precipitation of galena or arsenopyrite is a 

kinetically controlled, slow process, and, therefore, restoration of a complete equilibrium state 

following perturbation of the system can take years or decades or even longer, depending on the 

parameters of the system. Thus, desorption of initially sorbed matter is the dominant process in 

mobilizing contaminants in the short term. 

The relative importance of dissolution/precipitation versus adsorption/desorption is controlled by 

many factors including adsorption parameters and aquifer mineralogy, but also reaction kinetics, 

aqueous complexation processes, and solubility constants. For example, the role that 

adsorption/desorption plays in the mobilization of hazardous constituents depends highly on the 

total number of adsorption sites, which is the product of adsorption site density and specific 

surface area of the sorbing minerals. A sensitivity run (Section 4.6.1.2) with lower site density 

reveals that the increases in aqueous Pb and As are much less significant, because desorption is 

less relevant in mobilizing contaminants. 
 

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Different Host Rock Mineralogies

A suite of “representative” host rock mineralogies was simulated in a sensitivity study 

representing broad ranges of aquifer host rock compositions found throughout the United States 

(Section 4.7). In addition to the Coastal Plain Sandstone aquifer considered in the base case, we 

evaluated two other representative cases: (1) a mineral composition representative of a St Peter 

Sandstone and (2) a mineral composition representative of a Dolomitic Limestone. The St Peter 

Sandstone is composed primarily of quartz sand and has a poor pH buffering capacity, whereas 

the dolomitic limestone is dominated by calcite and dolomite, and has a good pH buffering 

capacity. Note that the host rock mineralogies differ with respect to major and accessory 

minerals, including clays and other adsorbents, but all have the same (small) volumetric fractions 

of trace minerals, including those containing Pb and As as essential components. The following 

conclusions can be drawn from the comparison of different host rocks: 

• Even small amounts of calcite or dolomite help moderate the pH changes caused by CO2

intrusion. The typical range of pH observed in our simulations is between 5.5 and 6, whereas 

values as low as 4.5 could be expected in the absence of buffering from aquifer minerals. 

Buffer capacity is effective as long as these minerals have not completely dissolved. In other 

words, the actual volumetric fraction of carbonate minerals is less relevant for buffer 

capacity, as long as the minerals remain present in the host rock. The St Peter Sandstone 

represents the most vulnerable aquifer, because the small initial amount of carbonate 

minerals is depleted after some time and the pH buffer capacity is lost. The dolomitic 

limestone is less vulnerable from the perspective of long-term effects, because it contains 

large amounts of carbonate minerals, which can can buffer pH for a long time. 

• While buffer capacity is relevant, differences in adsorption/desorption behavior have equal or 

more important effects on the resulting contaminant concentrations. The host rocks 

considered in this study differ with respect to clay minerals and clay volumetric fractions. 

The dolomitic limestone, for example, results in higher As concentrations after 100 years of 

CO2 intrusion, because it contains more clay minerals and thus releases more initially sorbed 

As into the groundwater, despite the fact that carbonate minerals provide long-term buffering 

capacity. 
 

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Oxidizing Versus Reducing Conditions

Although most groundwaters analyzed in Section 3 suggest reducing conditions, one sensitivity 

run was conducted to evaluate the potential for Pb contamination in an oxidized aquifer. The 

mineralogical composition of an oxidized aquifer was developed in Section 3.6.3.2, with the 

most relevant differences compared to the base case that pyrite is oxidized and cerussite instead 

of galena controls the aqueous concentration of Pb. The simulated behavior of the oxidized 

aquifer is very different from an aquifer in a reducing environment. Because of the much higher 

solubility of cerussite, the initial aqueous Pb concentration is already considerably higher than 

the MCL, even without considering CO2 leakage into the aquifer. This confirms the well-known 

fact that oxidative dissolution can lead to significant mobilization of hazardous constituents, an 

effect that is often more relevant than the decrease in pH caused by CO2 intrusion. A further 

increase in Pb concentration is predicted as CO2 enters the oxidized aquifer. As discussed in 

Section 4.8, Pb is mobilized near the CO2 intrusion region primarily from desorption. Further 

downstream, Pb precipitates out as cerussite, stabilizing the downstream Pb concentrations. 

Sensitivity to Other Geochemical Parameters and Conditions 

• It is known that the kinetic rate of mineral dissolution/precipitation is highly uncertain. 

Sensitivity simulations have been conducted varying the kinetic rate by two orders of 

magnitude. However, the simulated concentrations of aqueous Pb and As after 100 years of 

CO2 intrusion are rather insensitive to a variation of the kinetic rate, due to the dominance of 

adsorption/desorption processes. Strong sensitvities were observed for simulation case in 

which sorption processes were neglected, with aqueous concentrations of As increasing 

significantly with an increase in the kinetic rate. 

• The solubilities of hazardous-constituent-bearing minerals and the association constant used 

in the aqueous complexation of Pb are critical geochemical parameters that are not well 

defined in all cases. In exploratory sensitivity runs we conducted, these parameters were 

varied based on the variability of values found in the literature. Changes in the solubilities of 

galena and arsenopyrite (by about a half-order of magnitude) give rise to strong differences 

in the resulting aqueous concentrations of Pb and As. As discussed in Sections 4.6.4, these 
 

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differences result in part from the different dissolution behavior, but more importantly are 

caused by changes in the adsorption/desorption characteristics (the initial distribution of Pb 

and As in the aqueous phase and adsorbed on mineral surfaces changes with a variation of 

solubility). A variation in the PbS(aq) association constant also leads to significant changes 

in aqueous Pb concentrations after CO2 intrusion. 

• The initial volume fraction of galena was increased by three orders of magnitude in a 

sensitivity run, to understand the consequences of natural variability in trace-element 

minerals (Section 4.6.5.1). However, the changes in aqueous Pb concentrations were 

negligibly small in this sensitivity case, mainly because Pb mobilization is dominated by 

desorption processes, not by galena dissolution. In contrast to what was observed for Pb, an 

increase in the arsenopyrite volume fraction (by two orders of magnitude) gives rise to a 

moderate increase in the aqueous As concentrations, mainly because the dissolution kinetics 

are assumed to change with variation in the reactive surface area. 

• One sensitivity run was conducted to evaluate the impact of a comparatively high initial 

partial pressure of CO2 in the groundwater (Section 4.6.6). This has minor impact on the 

resulting aqueous Pb concentrations, but causes significant increases in As concentrations, 

both at initial conditions and after intrusion of CO2. 

Sensitivity to Hydrogeologic Conditions and CO2 Leakage Rate 

• The maximum amount of CO2 dissolved in water is a function of fluid pressure. Thus, in a 

shallower aquifer, fewer CO2-related pH changes and, consequently, smaller increases in 

aqueous Pb and As concentrations would be expected. A sensitivity run using an initial fluid 

pressure of 3 bar (30 m head), instead of 5 bar (50 m head) as in the base model, showed 

moderate impact, slightly decreasing the change in pH as well as the resulting contaminant 

concentrations compared to the base case (Section 4.6.7). 

• A sensitivity run with a flow rate of 1 m per year (instead of 10 m per year in the base model) 

was conducted to study the effect of groundwater flow conditions on the evolution of 

hazardous constituents. One major effect of a reduced flow rate is that dissolution of CO2 is 
 

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less effective, because a smaller volume of groundwater undersaturated with respect to CO2

flows past the location where CO2 enters the aquifer. As a result, most of the leaking CO2

remains in the gas phase, migrates upward towards the top of the confined aquifer, and 

spreads out laterally. Initially modeled as a 2D depth-averaged process (Section 4.6.8), it 

became clear that this strongly buoyancy-driven behavior should be modeled in 3D (Section 

4.9.2). Except for the uppermost portion of the aquifer, where contamination spreads widely 

following the dissolution of the gaseous CO2 plume, the area affected by contamination is 

smaller than in the base case, because the transport of aqueous species is driven by diffusion 

rather than by advection (i.e., downstream transport of TIC, Pb, and As is minimal). The 

maximum concentrations are moderately higher than in the base case, because of less 

effective dilution. 

• Similar 3D effects occur in a sensitivity case with increased CO2 leakage rate (Section 4.9.3). 

Since the rate of CO2 entering from below exceeds the dissolution capacity of the 

groundwater, the gaseous plume rises to the top of the aquifer, thereby bypassing most of the 

groundwater body. The resulting impact on aqueous contamination is influenced by the 

general pattern of the CO2 plume (e.g., the spreading of the plume at the top of the aquifer) 

and the subsequent migration of CO2-charged and contaminated water further downstream. 

Maximum concentrations are moderately higher than in the base case, mostly at the top of the 

aquifer. 

• The above cases showing the evolution of larger CO2 plumes demonstrate that the rate of 

CO2 entering an aquifer has only a minor effect on the maximum contaminant 

concentrations. Once the groundwater reaches its solubility limit, which occurs at relatively 

small intrusion rates, there is no further increase in total inorganic carbon and acidity. 

Consequently, no further mobilization of hazardous constituents occurs once local 

equilibrium has been achieved. Excess CO2 remains in the gas phase and migrates elsewhere, 

thereby changing the spatial distribution of possible groundwater contamination, but the 

contamination level remains essentially the same. 
 

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Discussion of Conservatisms and Mitigating Effects 

Some assumptions made in the model setup and in the geochemical model represent conservative 

choices. Below, we list these choices and discuss the expected implications on the geochemical 

system. 

• A confined aquifer was considered, meaning that excess CO2 not dissolving into groundwater 

accumulates at the aquifer top, potentially causing widespread pH changes and mobilization 

of contaminants. Excess CO2 in an unconfined aquifer would escape into the vadose zone, 

causing no further harm. 

• Contaminant transport was modeled without consideration of hydrodynamic dispersion, 

meaning that less spreading and dilution of the dissolved CO2 plume occurs. This tends to 

maximize the contaminant concentrations predicted by the model. 

• CO2 continuously enters the aquifer for an extended period of time (100 years), implying that 

the CO2 leakage remains undetected and no remedial measures are undertaken. 

• While the reactive transport simulations demonstrate the potential for increased concentration 

of Pb and As, it is not clear that the problem would remain after pumping and standard 

treatment of the water for potable purposes. Water recovery from the aquifer would lead to 

degassing of CO2, probably causing supersaturation with respect to calcite, which would 

likely induce co-precipitation of these contaminants. 

 
 

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5. Analogues 

Both natural and man-made analogues of CO2 intrusion into groundwater may help us to better 

understand corresponding changes in water quality. The natural leakage of CO2 (plus additional 

acid gases in some cases) from geologic reservoirs into groundwaters is discussed in Section 5.1, 

with emphasis on the resulting groundwater chemical compositions (based on Lewicki et al., 

2006, 2007). While changes in groundwater chemistry were observed to be related to CO2 (and 

sulfur gas) leakage as caused by acidification and interaction with host rocks along flow paths, 

waters remained potable in most cases. Section 5.2 describes the geochemical impacts of CO2

releases from a landfill into a shallow aquifer, causing cadmium levels in the groundwater to 

increase to levels above the maximum contaminant level of 5 ppb (McGrath et al., 2007) 

5.1 Natural Analogues 

5.1.1 San Vittorino, Italy 

The San Vittorino plain, located 100 km northeast of Rome in central Italy, is a sparsely 

populated intramontane basin that is used primarily for agriculture. Carbonate rocks are 

prevalent in the region (e.g., bedrock limestones and surface travertines). Numerous CO2-rich 

mineral springs, gas vents, and sinkholes, many of which pose hazards to local infrastructure, are 

located throughout the plain and are believed to be associated with migration of CO2 from depth 

along major faults in the area. In an effort to understand the influence of this CO2 leakage on 

groundwater chemistry and sinkhole formation, NASCENT (Natural Analogs for the Storage of 

CO2 in the geological ENvironmenT) (2005) conducted soil gas, bubble gas, dissolved gas, and 

groundwater geochemistry surveys throughout the plain. They found that anomalously high soil 

gas concentrations (up to 70% CO2, 80 ppm He, and 2% CH4) were closely correlated with the 

locations of sinkholes. Gas bubbles in springs, sinkholes, and wells had concentrations of CO2

from 36 to 85%, CH4 from 150 to 2100 mg L-1, He from 6 to 400 mg L-1, and N2 from 7 to 60%. 

H2S was also present, but unquantified. Dissolved gases had CO2 concentrations from <0.2 to 0.8 

L gas per L water at ambient pressure and temperature. The highest CO2 concentrations were 
 

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close to the solubility limit of CO2 in water at 1 atm pressure. Chemical analysis of waters 

collected from springs, wells, and sinkholes indicated that most are classified as Ca-HCO3

waters, while a few are classified as Ca-Mg-HCO3 waters. The total dissolved constituents varied 

widely between samples, with Total Dissolved Solids (TDS) ranging from 330 to 1843 mg L-1

and hardness ranging from 307 to 1843 mg L-1 as CaCO3. The concentrations of major elements 

were well correlated in samples. Concentrations ranged from 89 to 550 mg L-1 for Ca, 17 to 97 

mg L-1 for Mg, 10 to 795 mg L-1 for SO4, 43 to 1945 mg L-1 for B, and 230 to 2900 mg L-1 for 

Sr. Some waters were also enriched in Mn, Al, Zn, Pd, and Rb. Samples with the highest 

concentrations of dissolved constituents were located in the north-central part of the plain and 

also had the highest CO2 concentrations in both dissolved and excluded gases. The leakage of 

CO2 (and H2S) typically resulted in a 5 to 10 fold increase in most major and trace elements in 

springs. Despite elevated concentrations of dissolved chemical constituents in groundwaters, 

these waters are potable, according to current international groundwater standards for major ions. 

Correlations between dissolved chemical constituents in waters were interpreted to reflect the 

dissolution of carbonate rocks along flow paths of groundwaters that have become acidified by 

CO2 and H2S rising along high permeability pathways in fault zones. The formation of sinkholes 

was found to be closely associated with groundwaters having anomalously high dissolved 

chemical constituents. These features were likely formed by the dissolution of fine-grained rocks 

by acidified groundwaters and collapse triggered by earthquakes. NASCENT (2005) developed 

and deployed a continuous groundwater chemistry monitoring station in the San Vittorino plain, 

designed to detect anomalous changes that might serve as precursors to sinkhole formation. 

5.1.2 Florina Basin, Greece 

Two aquifers are present in the Florina basin. The first is a confined aquifer in clastic 

sedimentary rocks capped by silts and clays that extends throughout most of the basin and hosts 

the CO2 reservoir. The second is an unconfined aquifer hosted in karstic carbonates on the 

eastern and southern margins of the basin. To evaluate the effects of CO2 leakage from depth on 

groundwater quality in the Florina basin, Beaubien et al. (2004) conducted an investigation of the 
 

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chemistry of waters from wells and springs throughout the basin. They analyzed major element 

concentrations in 132 samples and trace element concentrations in 17 of these samples, and 

found that total dissolved solids ranged from 150 to 4230 mg L-1 and hardness ranged from 70 to 

1950 mg L-1 as CaCO3. Waters collected from shallow wells and springs were Ca-HCO3 type, 

while those collected from deep boreholes were Mg-HCO3 type. Most water samples were 

potable. However, samples collected close to the CO2 field had higher total dissolved chemical 

constituents and hardness, and consequently were of poorer drinking water quality. Beaubien et 

al. (2004) interpreted the spatial trends in the chemical compositions of groundwaters as 

reflecting the interaction of CO2-rich groundwaters with host rocks along flow paths. 

5.1.3 Albani Hills, Italy 

Three main hydrogeologic units have been identified in the Albani Hill region (Boni et al., 

1995). From depth to the surface, these include: (1) a calcareous-siliceous-marly basal complex 

constituting a multilayered aquifer hosted in fractured carbonates interbedded with confining 

clay layers, (2) a clayey-marly intermediate complex that acts as a groundwater flow boundary 

due to its low permeability, and (3) the Albani Hills volcanic complex, which displays wide 

variation in permeability and acts as a multilayered aquifer with radial groundwater flow. 

Chiodini and Frondini (2001) conducted a geochemical study of groundwaters collected from 

293 wells and 63 springs in the region (Figure 5.1), and found high CO2 partial pressures 

(average = 0.2 bar), indicating a high CO2 leakage rate into the regional aquifers. HCO3 is the 

major anion in most waters, with only a few samples characterized by high Cl and SO4

concentrations. The SO4-rich waters in the northern part of the study area are associated with an 

influx of deep sulfur-rich gases into shallow groundwaters, while the Cl-rich waters are located 

on the coastal plain and are associated with fossil seawater. The relative concentrations of 

cations (e.g., Na/K ratios, Figure 3.1) in waters reflect the interaction of CO2-rich groundwaters 

with host rocks. In particular, many samples have high HCO3, Ca, and Mg contents, which can 

be explained by the influx of CO2-rich fluids from depth and subsequent interaction with 

sedimentary rocks. 
 

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Fig. 5.1. Map of the Albani Hills region showing major drainage basins and locations of water samples 

from Chiodini and Frondini (2001).

5.1.4 Central Italy 

Throughout Tyrrhenian Central Italy, widespread nonvolcanic CO2 degassing (likely originating 

from a crustally-contaminated mantle or a mixture of magmatic and crustal components) occurs 

from vent and diffuse soil gas emissions and from CO2-enriched groundwaters (Chiodini et al., 

1999). From the Tyrrhenian Sea to the Apennine Mountains, buried structural highs act as gas 

traps. Widespread CO2 leakage occurs at the surface throughout the region in the form of diffuse 

and vent gas emissions or by dissolution in groundwaters and subsequent release at surface 
 

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springs. Chiodini et al. (1999, 2000, 2001) conducted geochemical investigations of regional 

aquifers and found that CO2 partial pressure values for springs are up to four orders of magnitude 

greater than that of the atmosphere. They also found that in geographic regions characterized by 

thick regional carbonate-evaporite aquifers, most or part of the deeply derived gas is dissolved 

by the aquifers, whereas in regions characterized by small aquifers, these aquifers cannot 

dissolve a large quantity of the CO2, and extensive vent and soil CO2 emissions occur at the 

surface. Chiodini et al. (2000) used mass-balance equations coupled with carbon isotopic 

analyses and hydrologic data to estimate fluxes of deeply derived CO2 of up to 0.29 g m-2d-1 into 

the aquifers. For waters with high CO2 partial pressures, the CO2 flux lost to the atmosphere is of 

the same order of magnitude as the influx of deep CO2. 

Based on the analysis of major dissolved chemical constituents in 52 springs, 65% were 

classified as Ca(Mg)-HCO3 in composition, 10% as Ca-Mg-SO4 in composition, 10% as Na-Cl 

in composition, and the remaining as intermediate in composition (Chiodini et al., 2000). A 

major process controlling the chemical composition of most groundwaters was the dissolution of 

calcite, while the dissolution of gypsum or halite was important in the springs of a few aquifers. 

Total dissolved inorganic carbon concentrations in springs ranged from 0.00244 to 0.05080 

mol/kg. 

5.1.5 Mammoth Mountain, California, USA 

Mammoth Mountain in eastern California has numerous CO2-rich cold springs located on its 

lower flanks, primarily associated with leakage from the high-pressure gas reservoir beneath the 

volcano (Evans et al., 2002). Evans et al. (2002) conducted a geochemical study of these springs 

to quantify the magmatic carbon discharge in cold groundwaters. Based on measured spring flow 

rates and concentrations and isotopic compositions of dissolved inorganic carbon in spring 

discharges, they found that Mammoth discharges 2×104

 t yr-1 of magmatic carbon (as CO2); this 

discharge varies seasonally from 30 to 90 t d-1. While most springs contain high concentrations 

of dissolved CO2, they are otherwise dilute, with specific conductance ranging from 100 to 300 

μS cm-1. 
 

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5.1.6 Paradox Basin, Utah, USA 

A number of natural springs and well bores discharge CO2-rich groundwater along the Little 

Grand Wash and Salt Wash faults in the Paradox basin of Utah. Samples collected from springs 

along the Little Grand Wash and Salt Wash faults and from Crystal Geyser were analyzed to 

determine the contents of major dissolved chemical constituents in groundwaters (Shipton et al., 

2004). Concentrations (in mmol L-1) were as follows: 2.37-20.83 for Ca, 7.86 to 9.30 for Mg, 

167.82-236.38 for Na, 7.62 to 10.44 for K, 0.00 to 0.14 for Fe, 0.00 to 0.02 for Mn, 0.01 to 0.16 

for Sr, 46.40-75.70 for HCO3, 91.15-204.52 for Cl, and 24.33-30.81 for SO4. Waters were high in 

total dissolved solids, which ranged from 13,555 to 21,188 mg L-1. Shipton et al. (2004) 

determined that the chemical compositions of groundwaters were primarily controlled by the 

dissolution of calcite and evaporites along groundwater flow paths. 

5.2 Landfill Gas Impacts 

McGrath et al. (2007) report on a study evaluating the potential impact of carbon dioxide 

contained in municipal solid waste (MSW) landfill gas on the release of cadmium from 

uncontaminated native soils. Elevated aqueous cadmium was detected in groundwater 

monitoring wells downgradient of the landfill but not in the landfill leachate or monitoring wells 

located upgradient. Based on soil leaching experiments and geochemical modeling, the authors 

concluded that the observed increases in aqueous cadmium in the field were likely caused by the 

CO2 dissolution into the groundwater and the related increase of acidity, causing mobilization of 

cadmium from the natural soils. Both the soil leaching experiments and the modeling results 

showed strong increases in cadmium concentrations with increasing partial pressure of CO2. 

Naturally occurring otavite (or cadmium carbonate) was reported to be the primary mineral 

controlling cadmium solubility, which suggests oxidizing conditions. In response to these 

findings, the landfill operator initiated a soil vapor extraction system in the vadose zone 

overlying the aquifer. Since then, aqueous cadmium concentrations in the formerly impacted 

wells have fallen to below detection limits (McGrath et al., 2007). 
 

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6. Overall Conclusions and Recommendations 

This report describes a systematic evaluation of the possible water quality changes in response to 

localized CO2 intrusion into aquifers currently used as sources of potable water in the United 

States. Our goal was to develop a general understanding of the potential vulnerability of United 

States potable groundwater resources in the event of CO2 leakage from deep geologic 

sequestration reservoirs. This goal was achieved in two main tasks, the first to develop a 

comprehensive geochemical model representing typical conditions in many freshwater aquifers 

(Section 3), the second to conduct a systematic reactive-transport modeling study to quantify the 

effect of CO2 intrusion into shallow aquifers for a variety of scenarios and aquifer conditions 

(Section 4). To ensure adequate model setup, the scenarios and conditions modeled in the 

reactive transport simulations—in particular those describing the geochemistry and mineralogy 

of potable aquifers—were selected based on the comprehensive geochemical model developed in 

Section 3. Detailed summaries of results from the two tasks are given, respectively, in Sections 

3.7 and 4.10. 

An important step in developing the comprehensive geochemical model in Section 3 was the 

identification of those mineral hosts containing hazardous constituents, which control their 

equilibrium concentration in potable groundwaters. Thermodynamic controls were evaluated 

using geochemical data for 38,000 potable groundwaters extracted from the USGS NWIS 

database, using a distribution-of-species code to test for their thermodynamic saturation in 

solution. This necessitated the iterative improvement and augmentation of a large supporting 

thermodynamic database comprising the solubility products of minerals of interest, and of 

complexes that affect the concentrations of the hazardous constituents in aqueous solution. 

Considering a representative groundwater under reducing conditions (a condition met in the large 

majority of all evaluated groundwaters), the most likely solubility-controlling mineral host for Pb 

is galena, while As would typically be controlled by the arsenopyrite component as a solid 

solution in pyrite. These mineral hosts are assumed to partially or completely control the initial 

aqueous concentrations of hazardous constituents as well as subsequent changes due to the 

ingress of CO2. The thermodynamic controls obtained for other hazardous constituents are 
 

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summarized in Section 3.7, together with other necessary input for the reactive transport 

simulations such as the evaluation of relevant aquifer conditions (e.g., oxidation state, initial CO2

concentrations in the aquifer) and the definition of the bulk/trace-element mineralogy in typical 

aquifer host rocks. 

Starting with the comprehensive geochemical model, a preliminary assessment of CO2-related 

water quality changes was conducted by calculating the expected equilibrium concentrations of 

hazardous inorganic constituents as a function of the partial pressure of CO2. P(CO2) was varied 

from 10-4 bar to 10 bar, leading to a corresponding pH variation between 8.9 and 5.6. 

Equilibrium concentrations were determined using a distribution-of-species code, assuming a 

representative groundwater in reducing conditions and incorporating the thermodynamic controls 

identified in above-mentioned database evaluation. Results indicate that the concentrations of 

some of the studied constituents, for example Ba, Cd, Sb, and Zn, are sensitive to changes in 

P(CO2), but that As, and to a lesser extent Pb and Zn, have shown the potential for exceeding 

their respective MCLs at elevated CO2 concentrations. In the systematic reactive-transport 

modeling study discussed below, primary emphasis was placed on the evaluation of the response 

and transport of As and Pb under intruding CO2. 

The reactive-transport simulator TOUGHREACT was used to predict the transport of gaseous 

and dissolved CO2 within a shallow aquifer, as well as induced geochemical changes caused by 

the presence of CO2, in particular those changes leading to the mobilization of Pb and As and 

their migration in the groundwater (Section 4). Our simulation results suggest that CO2 ingress 

into a shallow aquifer can lead to significant and long-lasting mobilization of Pb and As, 

contaminating the groundwater near the intrusion location and further downstream. However, 

while substantial increases in aqueous concentrations are predicted to occur compared to the 

initial water composition, the MCL for As in groundwater has been exceeded in only a few 

simulation cases, whereas the MCL for Pb has not been exceeded at all. 

Various sensitivity cases were simulated for different hydrological, geochemical, and 

mineralogical conditions, (1) to account for a range of conditions found in domestic potable 

aquifers and their host rocks, and (2) to evaluate the uncertainty associated with geochemical 
 

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processes and model parameters, results of which are summarized in Section 4.10. 

Adsorption/desorption via surface complexation is arguably the most important process 

controlling the fate of hazardous constituents mobilized by CO2 leakage when sorbing minerals 

are present. In a simulation case without adsorption/desorption taken into account, the resulting 

long-term contamination of the aquifer is less significant near the region where CO2 enters, 

whereas aqueous concentrations keep increasing with travel distance (eventually exceeding 

MCLs), as acidic water migrates further and more dissolution occurs. The relative importance of 

dissolution/precipitation versus adsorption/desorption is controlled by many factors, including 

adsorption parameters and aquifer mineralogy, reaction kinetics, aqueous complexation 

processes, and mineral solubility constants. For example, the role that adsorption/desorption 

plays in the mobilization of hazardous constituents depends highly on the total number of 

adsorption sites. A sensitivity run with lower site density reveals that early increases in aqueous 

Pb and As are much less significant, because desorption is less relevant in mobilizing 

contaminants. 

Moderate to strong sensitivities in the resulting Pb and As concentrations have been obtained for 

several other geochemical parameters (such as the solubilities of hazardous-constituent-bearing 

minerals, the kinetic rates for precipitation and dissolution, and the aqueous complexation 

constants) and conditions (such as the redox environment, with the simulated behavior in a 

oxidized aquifer being very different from an aquifer in a reducing environment). With respect to 

the typical aquifer mineralogies assumed in our study (ranging from almost pure arenites to 

dolomitic limestone), the most important effects resulted from differences in clay content, which 

affect the adsorption/desorption behavior, as well as from differences in the presence of 

carbonate minerals, which can moderate the pH changes caused by CO2 intrusion. An increase in 

the leakage rate does not necessarily lead to increases in the maximum contaminant 

concentrations in the aquifer. Once the groundwater reaches its solubility limit with respect to 

CO2, which occurs at relatively small intrusion rates, there is no further change in total inorganic 

carbon and acidity. Consequently, no further mobilization of hazardous constituents occurs once 

local equilibrium has been achieved. Excess CO2 remains in the gas phase and rises to the top of 

the aquifer, thereby bypassing most of the groundwater body. The resulting impact on aqueous 
 

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contamination is then influenced by the general pattern of the CO2 plume (e.g., the spreading of 

the plume at the top of the aquifer in the case of a confined aquifer) and the subsequent migration 

of CO2-charged and contaminated water further downstream. 

As stated before, the goal of this study was to better understand, in a global sense, the 

vulnerability of representative and relevant aquifer types in the case of CO2 intrusion, thereby 

providing sound scientific information to regulators and the public. It is understood, however, 

that even the most comprehensive sensitivity study cannot encompass all possible aquifer 

conditions and CO2 intrusion scenarios. Neither can it account for all relevant uncertainties. 

While the geochemical evaluation conducted in Section 3 was extremely comprehensive, it was 

also pointed out that several assumptions, approximations, and compromises were necessary in 

this effort, mostly because of incomplete characterization of important parameters such as 

dissolution and dissociation constants. We recommend that our study should now be followed up 

with site-specific model predictions, and that laboratory or field experiments should be 

performed to test uncertain model assumptions and parameters. Such experiments would involve 

exposing initially undisturbed groundwater in laboratory samples or in situ in the field to 

elevated levels of CO2. One could also attempt to test the developed geochemical models using 

measured data from natural analogue sites, such as those described in Section 5. 

Our studies suggest that the fate of inorganic hazardous constituents in the presence of elevated 

CO2 levels ultimately depends on their initial abundance and distribution in the aquifer minerals. 

For example, contaminants released from adsorption sites are expected to cause short-term 

perturbation in aqueous heavy-metal concentrations, while contaminants in host minerals would 

be mobilized in slower, longer duration processes. As pointed out before, the required 

information on hazardous constituents and their distribution in the mineral hosts is not easy to 

come by, because relevant mineral hosts in the aquifer rocks are difficult to identify where the 

concentrations are too small to be detected with standard mineralogical characterization methods. 

Therefore, an indirect approach was developed to determine their initial abundance and 

distribution, based on evaluation of geochemical data that are more easily measurable (i.e., by 

using a large number of groundwater analyses to test for saturation with respect to postulated 
 

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mineral hosts in conjunction with a supporting literature review, and analyses of trace elements 

in soils and sediments). While the approach demonstrated a high level of consistency, we 

recommend corroborating our findings through direct measurements of the form, distribution, 

and association of trace hazardous constituents within associated rock samples, which would 

involve, for example, synchroton-based microspectroscopic analysis at extremely high spatial 

resolution. 

7. Acknowledgments 

We are grateful to Guoxiang Zhang and Dan Hawkes of LBNL for constructive review of this 

manuscript. We thank the US Environmental Protection Agency, Office of Water and Office of 

Air and Radiation, for funding this study under an Interagency Agreement with the U.S. 

Department of Energy at the Lawrence Berkeley National Laboratory, Contract No. DE-AC02-

05CH11231. Special thanks go to Jonathan Scott of the U.S. Geological Survey, Oklahoma City, 

Oklahoma, who provided indispensable contributions to this report by extracting and supplying 

over 38,000 groundwaters from the NWIS database. 

8. References 

Abdel-Gaward, A.M. and P.F. Kerr (1961), Urano-organic mineral association. American 

Mineralogist, 46, 402–419. 

Abdul-Samad, F.A., J.H. Thomas, P.A. Williams, and R.F. Symes (1982), Chemistry of 

formation of lanarkite. Mineralogical Magazine, 46, 499–501. 

Abercrombie, H.J., I.E. Hutcheon, J.D. Bloch, and P. de Caritat (1994), Silica activity and the 

smectite-illite reaction. Geology, 22, 539–542. 

Acero, P., J. Cama and C. Ayora, 2007. Rate law for galena dissolution in acidic environment. 

Chemical Geology, 245: 219-229. 

Adami, L.H. and E.G. King (1964), Heats and free energies of formation of sulfides of 

manganeses, iron, zinc, and cadmium. U.S. Bur. Mines Rept. Inv. 6495, 10 p. 

Akinfiev N.N., A.V. Zotov, and N.D. Shikina (1994), Experimental investigation and 

thermodynamic correlation in the Sb(III)-S(II)-O-H system. Geochem. Intern. 31, 27–40. 
 

331 of 352

Al-Farawati, R. and C.M.G. van den Berg (1999), Metal – sulfide complexation in seawater. 

Marine Chemistry 63, 331-352 

Anderko, A. and P.J. Shuler (1998), Modeling the formation of iron sulfide scales: Using 

thermodynamic simulation software. Unpublished Report, OLI Systems Inc., 108 American 

Road, Morris Plains, NJ 07950, USA. 20 p. 

Anfalt, T., D. Dyrssen, E. Ivanova, and D. Jagner (1968), State of divalent mercury in natural 

waters. Svensk Kemisk Tidskrift, 80(10), 340–342 [in Swedish]. 

Appelo, C.J.A., and D. Postma (1994), Geochemistry, Groundwater and Pollution. 

A.A.Balkema, Rotterdam, Netherlands. 

Apps, J.A. (2004), Re-evaluation of the thermodynamic properties of SiO2(aq) and those of H2O 

low-pressure silica polymorphs. Appendix A in: Data Qualification for Thermodynamic 

Data Used To Support THC Calculations. N. Spycher and J.A. Apps, Bechtel SAIC 

Company Report No. ANL-NBS-HS-000043 Rev 00B., 99 p. plus appendices. Prepared for 

U.S. Department of Energy, Office of Civilian Radioactive Waste Management by Bechtel 

SAIC Company, LLC, 1180 Town Center Drive, Las Vegas, Nevada 89144. 

Aravena, R., L. I. Wassenaar, and L. N. Plummer (1995), Estimating 14C groundwater ages in a 

methanogenic aquifer. Water Resources Research, 31(9), 2307–2317. 

Arthur, J.D., A. A. Dabous, and J. B. Cowart (2005), Water-rock geochemical considerations for 

aquifer storage and recovery: Florida case studies. In: Underground Science and Technology, 

C.-F. Tsang and J. A. Apps, eds., Development in Water Sciences, 52, Chapter 24., pp. 327–

339, Elsevier, Amsterdam. 

Babcan, J. (1976), Entstehung und Stabilitaet von Antimonmineralen im System Sb3+-S2--H+-

OH-. (Formation and stability of antimony minerals in the Sb3+-S2--H+-OH- system), 

Chemie der Erde, v. 35, p. 281-284. 

Back, W., B. B. Hanshaw, L.N. Plummer, P.H. Rahn, C.T. Rightmire, and M. Rubin (1983), 

Process and rate of dedolomitization; mass transfer and 14 C dating in a regional carbonate 

aquifer. GSA Bulletin, 94(12), 1415–1429. 

Baas Becking, L.G.M., L.R. Kaplan, and D. Moore (1960), Limits of the natural environment in 

terms of pH and oxidation- reduction potentials. J. Geol. 68, 243–284. 

Ball, M.C., and M.J. Casson (1977), Thermal studies on lead(II) salts-V. Enthalpies of formation 

of lead(II) oxide carbonates by scanning calorimetry. J. Inorg. Nucl. Chem., 39, 1456–1457. 

Ballirano, P. and A. Maras (2002), Refinement of the crystal structure of arsenolite, As2O3. 

Zeitschrift fur Kristallographie, 217, 177-178. 

Barbanson, L., D. Saulas, D., and J.C. Touray, (1985), Les blendes mercuriferes de la region de 

Cabezon de la Sal (Santander, Espagne). Bulletin Mineralogique, 108, 483-486. 

Barnes, D.A., C.E. Lundgren, and M.W. Longman (1992), Sedimentology and diagenesis of the 

St. Peter Sandstone, Central Michigan Basin, United States. American Association of 

Petroleum Geologists Bulletin, 76, 1507–1532. 

Barrett, T.J., and G.M. Anderson (1982), The solubility of sphalerite and galena in NaCl brines. 

Economic Geology, 77, 1923–1933. 
 

332 of 352

Barrett, T.J., and G.M. Anderson (1988), The solubility of sphalerite and galena in 1–5 m NaCl 

brines to 300oC. Geochimica Cosmochimica Acta, 52, 813–820. 

Barton P.B. Jr, and P. Toulmin(1966), Phase relations involving sphalerite in the Fe-Zn-S 

system. Econ. Geol., 61:815-849. 

Bayliss, P. (1977), Crystal structure refinement of a weakly anisotropic pyrite cubic model. 

American Mineralogist, 62, 1168–1172. 

Bayliss, P., and W. Nowacki (1972), Refinement of the crystal structure of stibnite, Sb2S3. 

Zeitschrift fur Kristallographie,135, 308–315. 

Beaubien, S.E., S. Lombardi, G. Ciotoli, A. Annunziatellis, G. Hatziyannis, A. Metaxas, and 

J.M. Pearce (2004), Potential hazards of CO2 leakage in storage systems—Learning from 

natural systems, GHGT7, Vancouver, Sept. 5–8. 

Behra, P., P. Bonnissel-Gissinger, M. Alnot, R. Revel, and J. J. Ehrhardt (2001), XPS and XAS 

study of the sorption of Hg(II) onto pyrite. Langmuir, 17, 3970–3979. 

Belcher, R.W., A. Rozendaal‚ and W. J. Przybylowicz (2004), Trace element zoning in pyrite 

determined by PIXE elemental mapping: evidence for varying ore-fluid composition and 

electrochemical precipitation of gold at the Spitskop deposit, Saldania Belt, South Africa. XRay Spectrom., 33, 174–180. 

Benoit, J.M., C. C. Gilmour, R. P. Mason, and A. Wheyes (1999), Sulfide controls on mercury 

speciation and bioavailability to methylating bacteria in sediment pore waters. Environ. Sci. 

Technol., 33, 951–957. 

Berner, R.A. (1967), Thermodynamic stability of sedimentary iron sulfides. Am J Sci., 265 (9), 

773–785. 

Bessinger, B. (2000), The geochemistry of gold, arsenic, and antimony in the Carlin-type gold 

deposits and the mechanics of geologic fractures. Ph.D. Thesis, University of California, 

Berkeley, California. Ernest Orlando Lawrence Berkeley National Laboratory, Report LBNL46525, 2000. 

Bethke, P.M. and P.B. Barton, Jr. (1971), Sub-solidus relations in the system PbS-CdS. 

American Mineralogist, 56, 2034–2039. 

Bexfield, L.M. and Plummer, L.N., (2003), Occurrence of arsenic in ground water of the Middle 

Rio Grannde Basisn, central New Mexico. Chapter11 in Arsenic in Ground Water, A.H. 

Welch and K.G. Stollenwerk, eds., Kluwer Academic Publishers, Boston, pp. 295-327. 

Bigham, J.M., L. Carlson, and E. Murad (1994), Schwertmannite, a new iron oxyhydroxysulphate from Pyhasalmi, Finland, and other localities. Mineralogical Magazine; 58(4), 641–

648. 

Birkholzer, J.T., J.A. Apps, Y. Zhang, L. Zheng, and T. Xu (2008): Prediction of Groundwater 

Quality Changes in Response to CO2 Leakage from Deep Geological Storage, Abstract in: 

Proceedings 7th Annual Conference on Carbon Capture and Sequestration, Pittsburgh, PA, 

May 2008 

Blake, R L, R.E. Hessevick, T. Zoltai, and L.W. Finger (1966), Refinement of the hematite 

structure. American Mineralogist, 51, 123–129. 
 

333 of 352

Blanchard, M., M. Alfredsson, J. Brodholt, K. Wright, and C.R.A. Catlow (2007), Arsenic 

incorporation into FeS2 pyrite and its influence on dissolution: A DFT study. Geochimica et 

Cosmochimica Acta, 71, 624–630. 

Blatt, H. (1992), Sedimentary Petrology. W.H. Freeman and Company, New York. 2nd Edition, 

514 p. 

Boggs, S., Jr. (2003), Petrology of Sedimentary Rocks. The Blackburn Press, Caldwell, New 

Jersey. Reprint of the 1st Edition of 1992. 707 p. 

Boivin, J.-C., D. Thomas, and G. Tridot (1968), Etude chimique et cristallographique du sulfate 

de plomb dibasique. C. R. Acad. Sci. Paris, 267, Series C, 532–535. 

Bonazzi, P, S. Menchetti, and G. Pratesi (1995), The crystal structure of pararealgar. American 

Mineralogist, 80, 400–403. 

Bonazzi, P, S. Menchetti, and C. Sabelli (1987), Structure refinement ofkermesite: symmetry, 

twinning, and comparison with stibnite. Neues Jahrbuch fur Mineralogie, Monatshefte 1987, 

557–567 

Boni, C., P. Bono, S. Lombardi, L. Mastrorillo, and C. Percopo (1995), Hydrogeology, fluid 

geochemistry, and thermalism. In: The Volcano of the Alban Hills, Rome, R. Triglia, ed., pp. 

221–242. 

Bostick, B.C., S. Fendorf, and M. Fendorf (2000), Disulfide disproportionation and CdS 

formation upon cadmium sorption on FeS2. Geochimica et Cosmochimica Acta, 64(2) 247-

255. 

Bothe, J.V. and P.W. Brown (1999), The stabilities of calcium arsenates. J. Hazard. Mat., B69, 

197–207. 

Bowers, T., J. Jackson, and H. Helgeson (1985), Equilibrium Activity Diagrams. Elsevier Press, 

New York. 

Bradbury, M.H. and B. Baeyens (2005), Modelling the sorption of Mn(II), Co(II), Ni(II), Zn(II), 

Cd(II), Eu(III), Am(III), Sn(IV), Th(IV), Np(V) and U(VI) on montmorillonite: Linear free 

energy relationships and estimates of surface binding constants for some selected heavy 

metals and actinides. Geochimica et Cosmochimica Acta, 69(4), 875–892. 

Bradl, H.B. (2004), Adsorption of heavy metal ions on soils and soils constituents. Journal of 

Colloid and Interface Science, 277, 1–18. 

Bragg, L.J., J.K. Oman, S.J. Tewalt, C.L. Oman, N.H. Rega, P.M. Washington, and R.B. 

Finkelman (1998), The U.S. Geological Survey Coal Quality (COALQUAL) DataBase‚Version 2.0. U.S. Geological Survey Open-File Report 97-134, one CD-ROM. 

Brantley, S.L. and M.A. Velbel (1993), Geochemical kinetics of mineral-water reactions in the 

field and the laboratory. Chem. Geol., 105(1-3): 7-9. 

Brasseur (1940), Etude roentgenographique de la laurionite PbOHCI. Bull. Soc. Roy. Sc. Liege, 

9, 166-169. 

Brownfield, M.E., R.H. Affolter, J.D. Cathcart, S.Y. Johnson, I.K. Brownfield, and C.A. Rice 

(2005), Geologic setting and characterization of coals and the modes of occurrence of 

selected elements from the Franklin coal zone, Puget Group, John Henry No. 1 Mine, King 

County, Washington, USA. International Journal of Coal Geology, 63, 247–275. 
 

334 of 352

Buffle, J., R.R. deVitre, D. Perret, and G.G. Leppard (1988), Combining field measurements for 

speciation in non perturbable waters. In: Metal Speciation: Theory, Analysis and Application.

J.R. Kramer and E.E.Allen, eds., Lewis Publishers, Inc., 99–124. 

Buerger, M.J., W.A. Dollase, and I. Garaycochea-Wittke (1967), The structure and composition 

of the mineral pharmacosiderite. Zeitschrift fur Kristallographie, 125, 92–108. 

Buerger, M.J. (1939), The crystal structure of gudmundite. American Mineralogist 24, 183–184. 

Bur'yanova, E.Z., G.A. Kovalev, and A.A. Komkov (1957), The new mineral cadmoselite. 

Zapiski Vses. Mineralog. Obshch., 86, 626–628 (in Russian). 

Caracas, R. and X. Gonze (2005), First-principles study of the electronic properties of A2B3 

minerals, with A=Bi,Sb and B=S,Se. Physics and Chemistry of Minerals, 32, 295–300. 

Carroll, S.A., P.A. O’Day, and M. Piechowski (1998), Rock-water interactions controlling zinc, 

cadmium, and lead concentrations in surface waters and sediments, U.S. Tri-State Mining 

District. 2. Geochemical Interpretation. Environ. Sci. Technol., 32, 956–965. 

Catti, M, G. Chiari, and G. Ferraris (1980), The structure of ferrarisite, 

Ca5(HAsO4)2(AsO4)2*9H2O: Disorder, hydrogen bonding, and polymorphism with guerinite. 

Bulletin de Mineralogie 103, 541–546. 

Catti, M. and G. Ferraris (1974), Crystal structure of Ca5(HAsO4)2(AsO4)2*9H2O (guerinite). 

Acta Crystallographica, B30, 1789–1794. 

Catti, M, G. Ferraris, and G. Ivaldi (1981), The crystal structure of picropharmacolite, 

Ca4Mg(HAsO4)2(AsO4)2*11H2O. American Mineralogist, 66, 385–391. 

Catti, M, G. Ferraris, and G. Ivaldi G. (1977), Hydrogen bonding in the crystalline state. 

Structure of talmessite,. Ca2(Mg,Co)(AsO4)2*2H2O, and crystal chemistry of related 

minerals. Bulletin de Mineralogie, 100, 230–236. 

Cesbron, F. and H.J. Schubnel (1968), Nouvelles donnees sur la penfieldite. Bull. Soc. Fr. 

Mineral. Cristallogr., 91, 407–408. 

Chai, L. and A. Navrotsky (1996), Synthesis, characterization, and energetics of solid solution 

along the dolomite-ankerite join, and implications for the stability of ordered CaFe(CO3)2. 

Am. Mineral., 81, 1141–1147. 

Chase, M.W., C.A. Davies, J.R. Downey, D.J. Frurip, R.A. McDonald, and A.N. Syverud 

(1985), JANAF Thermochemical Tables, 3rd Edition. J. Phys.Chem. Data, 14 (Suppplement 

No. 1). 

Chen L., Zhang Q., Li D., and Wang G. (1993): Antimonselite - A new mineral. Acta 

Mineralogica Sinica 13(1), 7-11 (in Chinese with English abstract). 

Cherry, J.A., A.V. Shaikh, D.E. Tallman, and R.V. Nicholson (1979), Arsenic species as an 

indicator of redox conditions in groundwater. J. Hydrol., 43, 373–392. 

Chiodini, G., F. Frondini, D.M. Kerrick, J. Rogie, F. Parello, L. Peruzzi, and A.R. Zanzari 

(1999), Quantification of deep CO2 fluxes from Central Italy: Examples of carbon balance 

for regional aquifers and of soil diffuse degassing. Chem. Geol., 159, 205–222. 
 

335 of 352

Chiodini, G., F. Frondini, C, Cardellini, F. Parello, and L. Peruzzi (2000), Rate of diffuse carbon 

dioxide Earth degassing estimated from carbon balance of regional aquifers: The case of the 

central Apennine, Italy. J. Geophys. Res., 105, 8423–8424. 

Chiodini, G., F. Frondini, C. Cardellini, D. Granieri, L. Marini, and G. Ventura (2001), CO2

degassing and energy release at Solfatara volcano, Campi Flegrei, Italy, J. Geophys. Res., 

106, 16, 213–16,221. 

Chiodini G. and F. Frondini (2001), Carbon dioxide degassing from the Albani Hills volcanic 

region, Central Italy. Chem. Geol., 177, 67–83. 

Cowan, C. E. (1988), Review of selenium thermodynamic data: Final report. Electric Power 

Research Institute Report EPRI-EA-5655, 73 pp, Palo Alto, CA. 

Dai, Y. S. and J.M. Hughes (1989), Crystal-structure refinements of vanadinite and 

pyromorphite. The Canadian Mineralogist, 27, 189–192. 

Daskalakis, K.D. and G.R. Helz (1992), Solubility of CdS (greenockite) in sulfidic waters at 

25oC. Environ. Scl. Technol., 26, 2462–2468. 

Daskalakis, K.D. and G.R. Helz (1993), The solubility of sphalerite (ZnS) in sulfidic solutions at 

25°C and 1 atm pressure. Geoch. Cosmochim. Acta, 57, 4923–4931. 

Davies-Colley, R.J., P.O. Nelson, and K.J. Williamson (1985), Sulfide control of cadmium and 

copper concentrations in anaerobic estuarine sediments. Marine Chemistry, 16, 173–186. 

DeKock, C.W. (1986), Thermodynamic properties of selected metal sulfates and their hydrates. 

U.S. Bureau of Mines Information Circular 9081. 59.p. 

Deore, S. and A. Navrotsky (2006), Oxide melt solution calorimetry of sulfides: Enthalpy of 

formation of sphalerite, galena, greenockite, and hawleyite. American Mineralogist, 91, 400–

403. 

Derriche, Z. and P. Perrot (1976), Etude thermodynamique des phses solides et liquide dans le 

systeme PbO-PbSO4. Revue de Chimie Minerale, 13(4), 310–323. 

Diehl, S.F., M.B. Goldhaber, and J.R. Hatch (2004), Modes of occurrence of mercury and other 

trace elements in coals from the warrior Weld, Black Warrior Basin, Northwestern Alabama. 

Internat. J. Coal Geol., 59, 193Ð208. 

Dixit, S. and J.G. Hering (2003), Comparison of arsenic(V) and arsenic(III) sorption onto iron 

oxide minerals: Implications for arsenic mobility. Environ. Sci. Technol., 37, 4182–4189 

Dragun, J. (1988), The Soil Chemistry of Hazardous Materials. Hazardous Materials Control 

Research Institute, Silver Spring, MD. 

Drew Ilger, J., W.A. Ilger, R.A. Zingaro, and M.S. Mohan (1987), Modes of occurrence of 

uranium in carbonaceous uranium deposits: Characterization of uranium in a South Texas 

(U.S.A.) lignite. Chemical Geology, 63, 197–216. 

Dubessy, J., M. Pagel, J.M. Beny, H. Christensen, B. Hickel, C. Kosztolanyi, and B. Poty (1988), 

Radiolysis evidenced by H2-O2-bearing fluid inclusions in three uranium deposits. 

Geochimica et Cosmochimica Acta, 52, 1155–1167. 

Dunn, P.J., D.R. Peacor, and N. Newberry (1980), Johnbaumite, a new member of the apatite 

group from Franklin, New Jersey. American Mineralogist, 65, 1143–1145. 
 

336 of 352

Dutkiewicz, A., J. Ridley, and R. Buick (2003), Oil-bearing CO2-CH4-H2O fluid inclusions: Oil 

survival since the Palaeoproterozoic after high temperature entrapment. Chemical Geology, 

194(1–3), 51–79. 

Dyrssen, D. and M. Wedborg (1991), The sulphur-mercury system in natural waters. Water Air 

Soil Pollut., 56, 507–519. 

Dyrssen, D. and K. Kremling (1990), Increasing hydrogen sulfide concentration and trace metal 

behavior in the anoxic Baltic waters. Marine Chemistry, 30, 193–204. 

Dyrssen, D. (1989), Biogenic sulfur in two different marine environments. Marine Chem., 28, 

241–249. 

Dyrssen, D. and M.Wedborg, (1989), The state of dissolved trace sulphide in seawater. Marine 

Chem., 26, 289–293. 

Dyrssen, D. (1988), Sulphide complexation in surface seawater. Marine Chem., 24, 143–153. 

Dyrssen, D. (1985), Metal complex formation in sulphidic seawater. Marine Chem., 15, 285–

293. 

Dzombak, D.A. and F.M.M. Morel (1990), Surface Complexation Modeling—Hydrous Ferric 

Oxide. John Wiley & Sons, New York, 393 pp. 

Earley, J.W. (1950), Description and syntheses of the selenide minerals. American Mineralogist, 

35, 337–364. 

Earnshaw, A. and N. Greenwood (1997), Chemistry of the Elements, 2nd edition. ButterworthHeinemann, (1997). ISBN 0-7506-3365-4, Appendix 4, Abundance of Elements in Crustal 

Rocks. 

Edmunds, W.M., A.H. Bath, and D.L. Miles (1982), Hydrochemical evolution of the East 

Midlands Triassic sandstone aquifer, England. Geochimica et Cosmochimica Acta 46, 2069–

2081. 

Ehrhardt, J-J., P. Behra, P. Bonnissel-Gissinger, and M. Alnot (2000), XPS study of the sorption 

of Hg(II) onto pyrite FeS2. Surf. Interface Anal., 30, 269–272. 

Esdaile, J.D. (1966), The lead oxide-lead sulfate system. J. Electrochem. Soc., 113(1), 71–75. 

Evans, W.C., M.L. Sorey, A.C. Cook, B.M. Kennedy, D.L. Shuster, E.M. Colvard, L.D. White, 

and M.A. Huebner (2002), Tracing and quantifying magmatic carbon discharge in cold 

groundwaters: Lessons learned from Mammoth Mountain, USA, J. Volcanol. Geotherm. 

Res., 114, 291–312. 

Felmy, A.R., D. Girvin, and E.A. Jenne (1984), MINTEQ: A computer program for calculating 

aqueous geochemical equilibria. U.S. Environmental Protection Agency Report EPA-600/3-

84-032. 

Ferraris, G. and G. Chiari (1970), The crystal structure of CaHAsO4 (weilite). Acta 

Crystallographica, B26, 403–409. 

Ferraris, G., D.W. Jones, and J. Yerkess (1972), A neutron and X-ray refinement of the crystal 

structure of CaHAsO4*H2O (haidingerite). Acta Crystallographica, B28, 209–214. 
 

337 of 352

Ferraris, G., D.W. Jones, and J. Yerkess (1971), Determination of hydrogen atom positions in the 

crystal structure of. pharmacolite, CaHAsO4(H2O)2, by neutron diffraction. Acta 

Crystallographica, B27, 349–354. 

Fialips, C.-I., D. Huo, L. Yan, J. Wu, and J.W. Stucki (2002b), Effect of iron oxidation state on 

the IR spectra of Garfield nontronite. American Mineralogist, 87, 630-641. 

Finkelman, R.B. (1981), Modes of occurrence of trace elements in coal. U.S. Geological Survey 

Open-File Report 81–99. 301 p. 

Finkelman, R.B. and H. Klemic (1976), Brannerite from the Penn Haven Junction uranium 

occurrence, Carbon County, Pennsylvania. U.S. Geol. Surv. J. Res., 4(6), 715–716. 

Fish, W. (1993), Sub-surface redox chemistry: A comparison of equilibrium and recction-based 

approaches. Chapter 3 in Metals in Groundwater (Herbert E. Allen, E. Michael Perdue, 

David S. Brown, Eds). CRC, 464 p. 

Fleet, M.E., P.J. MacLean, and J. Barbier (1989), Oscillatory-zoned As-bearing pyrite from 

strata-bound and stratiform gold deposits: An indicator of ore fluid evolution. Econ. Geol. 

Monogr. 6, 356Ð362. 

Flis, J., M. Manecki, and T. Bajda (2007), Solubility of pyromorphite-mimetite solid solutions at 

5–65oC: Variability of thermodynamic stability of minerals from pyromorphite-mimetite 

series at 5–65oC. Goldschmidt Conference Abstracts. 

Focazio, M.J., Tipton, D., Dunkle Shapiro, S., and L.H. Geiger (2006), The chemical quality of 

self-supplied domestic well water in the United States. Ground Water Monitoring & 

Remediation, 26(3), pages 92–104. 

Framson, P.E. and J.O. Leckie (1978), Limits of coprecipitation of cadmium and ferrous sulfides. 

Environmental Science & Technology, 12(4), 465–469. 

Frueh, A J. (1951), The crystal structure of claudetite (monoclinic As2O3). American 

Mineralogist, 36, 833–850. 

Giannetti, B.F., W.A. Moreira, S.H. Bonilla, C.M.V.B. Almeida, and T. Raboczkay (2006), 

Towards the abatement of environmental mercury pollution: An electrochemical 

characterization. Colloids and Surfaces A: Physicochem. Eng. Aspects, 276, 213–220. 

Giordano, T.H. and H.L. Barnes, (1979), Ore solution chemistry IV: PbS solubility in bisulfide 

solutions to 300°C. Econ. Geol., 74, 1637–1646. 

Girard, J.-P. and D.A. Barnes (1995), Illitization and paleothermal regimes in the Middle 

Ordovician St. Peter Sandstone, central Michigan Basin: K-Ar, oxygen isotope, and fluid 

inclusion data. AAPG Bulletin, 79(1), 49–69. 

Gluskoter, H.J. and P.C. Lindahl (1973), Cadmium: Mode of occurrence in Illinois coals. 

Science, 181, 264–266. 

Goldberg, S. (2002), Competitive adsorption of arsenate and arsenite on oxides and clay 

minerals. Soil Sci. Soc. Am. J., 66, 413–421. 

Goldberg, S., S.M. Lesch, D.L. Suarez and N.T. Basta (2005), Predicting arsenate adsorption by 

soils using soil chemical parameters in the Constant Capacitance Model. Soil Sci. Soc. Am. 

J., 69(5), 1389–1398. 
 

338 of 352

Goldhaber, M.B., R.C. Lee, J.R. Hatch, J.C. Pashin, and J. Treworgy (2003), Role of large scale 

fluid flow in subsurface arsenic enrichment. Chapter 5 in Arsenic in Ground Water, A.H. 

Welch and K.G. Stollenwerk, eds., Kluwer Academic Publishers, Boston, pp. 127–164. 

Golightly D.W. and F.O. Simon (Eds) (2006), Methods for Sampling and Inorganic Analysis of 

Coal. U.S. Geological Survey Bulletin 1823 

Greensmith, J.T. (1989), Petrology of the Sedimentary Rocks. Unwin Hyman, London. 7th

Edition, 262 p. 

Grundl, T.J. and D.L. Macalady (1989), Electrode measurements of redox potential in anaerobic 

ferric/ferrous chloride systems. J. Contam. Hydrol. 5, 97–117. 

Gu, X. and L.J. Evans (2007), Modelling the adsorption of Cd(II), Cu(II), Ni(II), Pb(II), and 

Zn(II) onto Fithian illite. Journal of Colloid and Interface Science, 307, 317–325 

Gubeli, A.O. and J. Ste-Marie (1967), Constantes de stabilité de thiocomplexes et produits de 

solubilité de sulfures de métaux. II Sulfure de zinc. Can. J. Chem., 45(18), 2101–2108. 

Hansen, H.C.B., O.K. Borggaard, and J. Sorensoen (1994), Evaluation of the free energy of 

formation of Fe(II)-Fe(III) hydroxide-sulphate (green rust) and its reduction of nitrite. 

Geochim. et Cosmochim. Acta, 58, 2599–2608. 

Helean, K.B., A. Navrotsky, G.R. Lumpkin, M. Colella, J. Lian, R.C. Ewing, B. Ebbinghaus, and 

J.G. Catalano (2003), Enthalpies of formation of U-, Th-, Ce-brannerite: implications for 

plutonium immobilization. Journal of Nuclear Materials, 320, 231–244. 

Helgeson, H.C., J.M. Delany, H.W. Nesbit, and D.K. Bird (1978), Summary and critique of the 

thermodynamic properties of minerals. American Journal of Science, 278A, 229 p. 

Helgeson, H.C. (1990), Note 18 to the SUPCRT SPRONS92.DAT database, Johnson et al., 

(1992). 

Hemley, J. J. (1953), A study of PbS solubility and its relation to ore deposition. Econ. Geol., 48, 

13-138. 

Heyding, R D, and R.M. (1976), Crystal structures of Cu1.8Se, Cu3Se2, alpha- and 

gamma=CuSe, CuSe2, and CuSe2II. Canadian Journal of Chemistry, 54, 841–848. 

Hill R. J. (1985), Refinement of the structure of orthorhombic PbO (massicot) by Rietveld 

analysis of neutron powder diffraction data. Acta Crystallographica, Section C, 41 p.1281-

1284. 

Hizal, J. and R. Apak, 2006. Modeling of copper(II) and lead(II) adsorption on kaolinite-based 

clay minerals individually and in the presence of humic acid. Journal of Colloid and 

Interface Science, 295, 1–13. 

Hoholick, D.J. (1980), Porosity, grain fabric, water chemistry, cement, and depth of the St. Peter 

Sandstone in the Illinois Basin. University of Cincinnati, Master's Thesis, 72 p. 

Hoholick, J.D., T. Metarko, and P.E. Potter (1984), Regional variations of porosity and cement: 

St. Peter and Mount Simon Sandstones in Illinois Basin. American Association of Petroleum 

Geologists Bulletin, 68, 753–764. 

Holland, T.J.B. and R. Powell (1998), An internally consistent thermodynamic data set for 

phases of petrological interest. Journal of Metamorphic Geology, 16(3), 309–343. 
 

339 of 352

Hopkins, J. and P. Putnam (2000), Fabric alteration and isotopic signature of carbonates in a 

recharge area of the Madison Aquifer, Montana, USA. Journal of Geochemical Exploration, 

69–70 (June 2000), 235–238. 

Hower, J.C. and J. D. Robertson (2003), Clausthalite in coal. International Journal of Coal 

Geology, 53, 219–225. 

Howie, R.A. and W. Moser (1968), Structure of Tin(II) "Hydroxide" and Lead(II) "Hydroxide". 

Nature, 219, 372 – 373. 

Huerta-Diaz, M.A. and J.W. Morse (1992), Pyritization of trace metals in anoxic marine 

sediments. Geochemica et Cosmochimica Acta, 56, 268l–2702. 

Humphreys, D.A., J.H. Thomas, and P.A. Williams (1980), The chemical stability of mendipite, 

diaboleite, chloroxiphite, and cumengeite, and their relationships to the secondary lead 

minerals. Mineralogical Magazine, 43, 901–904. 

HydroGeoLogic, Inc. and Allison Geoscience Consultants, Inc. (1999), MINTEQA2/ 

PRODEFA2, A Geochemical Assessment Model for Environmental Systems: User Manual 

Supplement for Version 4.0. Prepared for U.S. Environmental Protection Agency, National 

Exposure Research Laboratory, Ecosystems Research Division, Athens, Georgia, June 1998 

(revised September 1999). 76 p. Available at 

http://www.epa.gov/ceampubl/mmedia/minteq/index.htm

Irving, H.M.N.H. (1977), Dithizone. Analytical Science Monographs No. 5. The Chemical 

Society, London, 106 p. 

James, Noel P., Yvonne Bone, and T. Kurtis Kyser (1993), Shallow burial dolomitization and 

dedolomitization of mid-Cenozoic, cool-water, calcitic, deep-self limestones, Southern 

Australia. Journal of Sedimentary Petrology, 63(3) (May), 528–538. 

Jansen, M. (1977), Crystal structure of As2O5. Angew. Chem. Int. Ed. Engl., 16(5), 314–315. 

Janssens, A. (1973), Stratigraphy of the Cambrian and lower Ordovician rocks of Ohio. Bull. 64, 

Ohio Department of Natural Resources, Division of the Geological Survey. 

Kovacevic, D., A. Pohlmeier, G. Ozbas, H.D. Narres, and M.J.N. Kallay (2000), The adsorption 

of lead sepecies on goethite. Colloids and Surfaces A: Physicochem. Eng. Aspects, 166, 225–

233 

Kuhn, J., F. Fiene, R. Cahill, H. Gluskoter, and N. Shimp (1981), Abundance of Trace and Minor 

Elements in Organic and Mineral Fractions of Coal. U.S. Environmental Protection Agency, 

Washington, D.C., EPA/600/7-80/003, 67 p. 

Riley, R.A., J.A. Harper, M.T. Baranoski, C.D. Laughrey, and R.W. Carlton (1993), Measuring 

and predicting reservoir heterogeneity in complex deposystems: the late Cambrian Rose Run 

Sandstone of Eastern Ohio and Western Pennsylvania. US Department of Energy. 

Johnson, J.W., E.H. Oelkers, and H.C. Helgeson (1992), SUPCRT92: A software package for 

calculating the standard molal thermodynamic properties of minerals, gases, aqueous species, 

and reactions from 1 to 5000 bars and 0 to 1000 degrees C. Computers and Geosciences, 18, 

899–947. 
 

340 of 352

Johnston, R.B. and P.C. Singer (2007), Solubility of symplesite (ferrous arsenate): Implications 

for reduced groundwaters and other chemical environments. Soil Sci Soc. Am. J., 71, 101–

107. 

Kelley, K.K. (1960), High-Temperature Heat-Content, Heat-Capacity, and Entropy Data for the 

Elements and Inorganic Compounds. Contributions to the Data on Theoretical Metallurgy 

XIII. Bulletin, 584. Washington, D.C.: U.S Government Printing Office. 232 p. 

Kellog H. H. and S.K. Basu (1960), Thermodymamic properties of the system Pb-S-O to 

1100K. Trans TMS-AIME, 218(2): 70-81. 

Kellog, H.H. (1989), Critical evaluation of the thermochemical properties of lead sulfates. 

Metallurgical Trans. B, 20B, 77–85. 

Kitahama, K, R. Kiriyama, and B. Yoshihisa (1975), Refinement of the crystal structure of 

scorodite. Acta Crystallographica B31, 322–324. 

Kiukkola, K. and C. Wagner (1957), Measurements on galvanic cells involving solid 

electrolytes. Journal of the Electrochemical Society, 104 (6), 379–387. 

Kolker, A., S.J. Mroczkowski, C.A. Palmer, K.O. Dennen, R.B. Finkelman, and J.H. Bullock, Jr. 

(2002), Toxic xubstances from coal combustion- A comprehensive assessment, Phase II: 

Element modes of occurrence for the Ohio 5/6/7, Wyodak, and North Dakota coal samples. 

Open-File Report 02-224, U.S. Geological Survey, U.S. Department of the Interior. 79 p. 

Kolker, A., C.L. Senior, and J.C. Quick (2006), Mercury in coal and the impact of coal quality 

on mercury emissions from combustion systems. Applied Geochemistry, 21, 1821–1836. 

Kuzel, H.-J. (1973), Hydrothermalesynthese basicher Bleisulfate (hydrothermal synthesis of 

basic lead-sulfates). Neues Jahrbuch fur Mineralogie Monatshefte, 110–116. 

Kyono, A., M. Kimata, and T. Hatta (2005), Light-induced degradation dynamics in realgar: in 

situ structural investigation. using single crystal X-ray diffraction and X-ray photoelectron 

spectroscopy. American Mineralogist, 90, 1563–1570. 

Lackovic, K., M.J. Angove, J.D. Wells, and B.B. Johnson (2003), Modeling the adsorption of 

Cd(II) onto Muloorina illite and related clay minerals. Journal of Colloid and Interface 

Science, 257: 31-40. 

Langmuir, D., J. Mahoney, and J. Rowson (2006), Solubility products of amorphous ferric 

arsenate and crystalline scorodite (FeAsO4.2H2O) and their application to arsenic behavior in 

buried mine tailings. Geochim. et Cosmochim. Acta, 70, 2942–2956. 

Lasaga, A.C., J.M. Soler, J. Ganor, T.E. Burch and K.L. Nagy (1994), Chemical weathering rate 

laws and global geochemical cycles. Geochimica et Cosmochimica Acta, 58: 2361-2368. 

Lee, J.S. and J.O. Nriagu (2007), Stability constants for metal arsenates. Environ. Chem., 4, 123–

133. 

Lemoine, P., D. Carre, and F. Robert (1991), Structure du sulfure de fer et d'antimoine, FeSb2S4 

(Berthierite). Acta Crystallographica, C47, 938–940. 

Lennie, A R, S.A.T. Redfern, P.F. Schofield, and D.J. Vaughan (1995), Synthesis and Rietveld 

crystal structure refinement of mackinawite, tetragonal FeS. Mineralogical Magazine, 59, 

677–683. 
 

341 of 352

Levinson, A.A. (1977), Hydrogen—A reducing agent in some uranium deposits. Canadian 

Journal of Earth Sciences, 14, 2679–2681. 

Lewicki, J.L., J.T. Birkholzer, and C.-F. Tsang (2006), Natural and industrial analogues for 

release of CO2 from storage reservoirs: identification of features, events, and processes, and 

lessons learned, Lawrence Berkeley National Laboratory report, LBNL-59784. 

Lewicki, J.L., J.T. Birkholzer, and C.-F. Tsang (2007), Natural and industrial analogues for 

leakage of CO2 from storage reservoirs – identification of features, events, and processes and 

lessons learned, Journal of Environmental Geology, 52(3), pp. 457-467. 

Lide, D.R. (ed.) (2005), CRC Handbook of Chemistry and Physics, 85th Edition. CRC Press. 

Boca Raton, Florida (2005). Section 14, “Geophysics, Astronomy, and Acoustics; 

Abundance of Elements in the Earth's Crust and in the Sea.” 

Lindberg, R.D. and D.D. Runnels (1984), Ground water redox reactions: An analysis of 

equilibrium state applied to Eh measurements and geochemical modeling. Science, 225, 

925-927. 

Liu, J., Z. Li, J. Liu, J. Wang, C. Feng, and W. Lu (2005), Mineralogy of the stibniteantimonselite series in nature. Journal of Jilin University (Earth Science Edition), 35(5), 

545–553. 

Lozar, J., L. Schuffenecker, G. Cudey, and J. B. Bourdet (1984), Determination a 25°C des 

proprietes thermodynamiques des complexes chlores du plomb divalent a partir de mesures 

de solubilite dans des solutions aqueuses de force ionique inferieure a 1 mol kg-1. 

Thermochimica Acta, v. 79, Pages 171-186. 

Lutzenkirchen, J. (2006), Surface Complexation Modelling. Academic Press, Amsterdam, 

London, 638 p. 

Manning, B.A. and S. Goldberg (1997), Adsorption and stability of arsenic(III) at the clay 

mineral-water interface. Environ. Sci. Technol., 31, 2005–2011. 

Martinez Garcia, E., C. Luque, R. Burkhardt, R., and M. Guiterrez-Claverol (1991), Hozarco: Un 

ejemplo de mineralizacion de Pb-Zn-Hg de edad permica (Cordillera Cantabrica, NW de 

Espana). Boletin Sociedad Espanola de Mineralogica, 14, 107–118. 

Majzlan, J., B.E. Lang, R. Stevens, A. Navrotsky, B.F. Woodfield, and J. Boerio-Goates (2003), 

Thermodynamics of Fe oxides: Part I. Entropy at standard temperature and pressure and heat 

capacity of goethite (α-FeOOH), lepidocrocite (γ-FeOOH), and maghemite (γ-Fe2O3). 

American Mineralogist, 88(5–6), 846–854. 

Majzlan, J., K.D. Grevel, and A. Navrotsky (2003). Thermodynamics of Fe oxides: Part II. 

Enthalpies of formation and relative stability of goethite (α-FeOOH), lepidocrocite (γFeOOH), and maghemite (γ-Fe2O3). American Mineralogist, 88(5–6), 855–859. 

Majzlan, J., A. Navrotsky, and U. Schwertmann (2004), Thermodynamics of Fe oxides: Part III. 

Enthalpies of formation and stability of ferrihydrite (~Fe(OH)3), schwertmannite 

(~FeO(OH)3/4(SO4)1/8), and e-Fe2O3. Geochimica et Cosmochimica Acta, 68(5), 1049–1059. 

McGrath, A.E., G.L. Upson, and M.D. Caldwell (2007), Evaluation and mitigation of landfill gas 

impacts on cadmium leaching from native soils. Ground Water Monitoring & Remediation, 

27, 99–109. 
 

342 of 352

McKibben, M.A., B.A. Tallant and J.K.d. Angel (2008), Kinetics of inorganic arsenopyrite 

oxidation in acidic aqueous solutions. Applied Geochemistry, 23: 121-135. 

McMahon, P.B., and F.H. Chapelle (2008), Redox processes and water quality of selected 

principal aquifer systems, Ground Water, v. 46, no. 2, p. 259-271. 

McPhail, D.C., E. Summerhayes, S. Welch, and J. Brugger (2003), The geochemistry and 

mobility of zinc in the regolith. In: Advances in Regolith, I.C. Roach, ed., pp. 287–291, CRC 

for Landscape Environments and Mineral Exploration, PO Box 1130, Bentley, Western 

Australia 6102 Australia. ISBN 0-7315-4815-9 (CD-ROM). 

McPhail, D.C., E. Summerhayes, V. Jayaratne, and A. Christy (2006), Hemimorphite solubility 

and stability of low-T zinc minerals. Geochimica et Cosmochimica Acta, 70. 16th Annual 

Goldschmidt Conference, 27 August to 1 September, Melbourne, Australia. 

Mehra, M.C. and A.O. Gubeli (1970a), Complexing characteristics of insoluble selenides. II. 

Manganese selenide. Journal of the Less-Common Metals, 22(3), 281-285. 

Mehra, M.C. and A.O. Gubeli (1970b), Complexing characteristics of insoluble selenides. I. 

Silver selenide. Canadian Journal of Chemistry, 48, 3491–3497. 

Mehra, M.C. and A.O. Gubeli (1971), Complexing characteristics of insoluble selenides. III. 

Mercuric selenide. Journal of the Less-Common Metals, 25(3), 221–224. 

Michel, F.M., L. Ehm, S.M. Antao, P.L. Lee, P.J. Chupas, G. Liu, D.R. Strongin, M.A.A. 

Schoonen, B.L. Phillips, and J.B. Parise (2007), The structure of ferrihydrite, a 

nanocrystalline material. Science, published online 24 May 2007 (10.1126/science.1142525). 

Michel, F. M., L. Ehm, G. Liu, W.Q. Han, X.S.M. Antao, P.J. Chupas, P.L. Lee, K. Knorr, H. 

Eulert, J. Kim, C.P. Grey, A.J. Celestian, O.J. Gillow, M.A.A. Schoonen, D.R. Strongin, and 

J.B. Parise (2007), Similarities in 2- and 6-line ferrihydrite based on pair distribution 

function analysis of x-ray total scattering. Chem. Mater, 19, 1489–1496. 

Mills, K.C. (1974), Thermodynamic Data for Inorganic Sulfides, Selenides and Tellurides. 

Butterworths, London, 845 p. 

Min, M.-Z., Z.-W. Meng , G.-Y. Sheng , Y.-S. Min , and X. Liu (2000), Organic geochemistry of 

paleokarst-hosted uranium deposits, South China. Journal of Geochemical Exploration, 68, 

211–229. 

Mohan, M.S., R.A. Zingaro, R.D. Macfarlane, and K.J. Irgolic, K.J. (1982), Characterization of a 

uranium-rich organic material obtained from South Texas lignite. Fuel, 61, 853–858. 

Morimoto, N. and L.A. Clark (1961), Arsenopyrite crystal-chemical relations from Freiberg, 

Germany. American Mineralogist, 46, 1448–1469. 

Morin, G., G. Rousse, and E. Elkaim (2007), Crystal structure of tooeleite, 

Fe6(AsO3)4SO4(OH)4·4H2O, a new iron arsenite oxyhydroxy-sulfate mineral relevant to acid 

mine drainage. American Mineralogist; 92(1), 193–197. 

Mullen, D.J.E. and W. Nowacki (1972), Refinement of the crystal structures of realgar, AsS and 

orpiment, As2S3. Zeitschrift fur Kristallographie, 136, 48–65. 

Muller, B. and L. Sigg (1991), Adsorption of lead(ll) on the goethite surface: Voltammetric 

evaluation of surface complexation parameters. Journal of Colloid and Interface Science, 

148(2), 517–532. 
 

343 of 352

NASCENT (2005), Natural analogs for the geological storage of CO2, NASCENT Report 2005/6, 

March 2005. 

Nefedov, V.I., Yu.A. Teterin, A.M. Lebedev, A.Yu. Teterin, A.P. Dementjev, M. Bubner, T. 

Reich , S. Pompe, K.H. Heise, and H. Nitsche (1998), Electron spectroscopy for chemical 

analysis investigation of the interaction of uranyl and calcium ions with humic acids. 

Inorganica Chimica Acta, 273, 234–237. 

Nordstrom, D.K. (1984), Partial compilation and revision of basic data in the Wateq programs. 

U.S. Geol. Surv. WRI Rept. #84-4186, 40 p. 

Nordstrom, D.K. and D.G. Archer (2003), Arsenic thermodynamic data and environmental 

geochemistry. An evaluation of thermodynamic data for modeling the aqueous 

environmental geochemistry of arsenic. Chapter 1 in Arsenic in Ground Water, A.H. Welch 

and K.G. Stollenwerk, eds., Kluwer Academic Publishers, Boston, pp. 1–33. 

Odom, I. E., T.M. Willand, and R.J. Lassin (1977), Paragenesis of authigenic minerals in St. 

Peter Sandstone. American Association of Petroleum Geologists Bulletin, 61, 1384–1385. 

Paar, W.H., D. Topa, A.C Roberts, A.J Criddle, G. Amann, and R.J. Sureda (2002), The new 

mineral species brodtkorbite, Cu2HgSe2, and the associated selenide assemblage from 

Tuminico, Sierra de Cacho, La Rioja, Argentina. The Canadian Mineralogist, 40(1), 225–

237. 

Paces, T. (1978), Reversible control of aqueous aluminum and silica during the irreversible 

evolution of natural waters. Geochim. Cosmochim. Acta 42, 1487-1493. 

Pacheco, Fernando A.L., and T. Szocs (2006), Dedolomitization reactions driven by 

anthropogenic activity on loessy sediments, SW Hungary. Applied Geochemistry 21, 614–

631. 

Pagnanelli, F., L. Bornoroni, E. Moscardini, and L. Toro (2006), Non-electrostatic surface 

complexation models for protons and lead(II) sorption onto single minerals and their 

mixture. Chemosphere, 63, 1063–1073. 

Palache, C., H. Berman, and C. Frondel (1951), The System of Mineralogy, 7th edition, vol II. 

Halides, Nitrates, Borates, Sulfates, Phosphates, Arsenates, Tungstates, Molybdates, etc. 

John Wiley and Sons, Inc. New York. 

Palandri, J. and Y.K. Kharaka (2004), A compilation of rate parameters of water-mineral 

interaction kinetics for application to geochemical modeling. Open File Report 2004-1068, 

US Goelogical Survey. 

Palenik, C.S., S. Utsunomiya, M. Reich, S.E. Kesler, and R.C. Ewing (2004), Invisible gold 

revealed: direct imaging of gold nanoparticles in a Carlin-type deposit. Am. Mineral., 89, 

1359–1366. 

Pankratz, L.B. and E.G. King (1987), Thermodynamic properties of sulfides. U.S. Bur. Mines 

Bull., 689, 427 p. 

Paquette, K.E and G. Helz (1997), Inorganic speciation of mercury in sulfidic waters: the 

importance of zero-valent sulfur. Environmental Science and Technology, 31, 2148–2153. 

Paquette, K.E and G. Helz (1995), Solubility of cinnabar (red HgS) and implications for mercury 

speciation in sulfidic waters. Water, Air, and Soil Pollution, 80, 1053–1056. 
 

344 of 352

Parker, V.B. and I.L. Khodakovskii, (1995), Thermodynamic properties of the aqueous ions (2+ 

and 3+) of iron and key compounds of iron. J. Phys. Chem. Ref. Data, 24(5), 1699–1745. 

Parkhurst, D.L. and C.A.J. Appelo (1999), User’s guide to PHREEQC (version 2)—A computer 

program for speciation, batch-reaction, one-dimensional transport, and inverse geochemical 

calculations. U.S. Geological Survey Water-Resources Investigations Report 99-4259, 

Denver, CO, Http://Wwwbrr.Cr.Usgs.Gov/Projects/GWC_Coupled/Phreeqc/Html/Final.Html 

Pecharroman, C, T. Gonzalez-Carreno, and J.E. Iglesias (1995), The infrared dielectric properties 

of maghemite, gamma-Fe2O3, from reflectance measurement on pressed powders. Physics 

and Chemistry of Minerals, 22, 21–29 

Penn, R.L. (2007), Resolving an elusive structure. Science, published online 24 May 2007 

(10.1126/science.1144002). 

Perfetti E., G.S. Pokrovski, K. Ballerat-Busserolles, V. Majer, and F. Gibert (2008), Densities 

and heat capacities of aqueous arsenious and arsenic acid solutions to 350 °C and 300 bar, 

and revised thermodynamic properties of As(OH)3(aq), AsO(OH)3(aq) and iron sulfarsenide 

minerals. Geochimica et Cosmochimica Acta 72 (2008) 713–731 

Pertlik, F. (1975), Die kristallstruktur der monoklinen form von As2O3 (Claudetit II). 

Monatshefte fur Chemie, 106, 755–762. 

Pettijohn, F.J., P.E. Potter, and R. Siever (1987), Sand and Sandstone, Springer Verlag. 

Piatak, N.M., R.R. Seal, II, and J.M. Hammarstrom (2004), Environmental significance of 

cadmium and other trace-element concentrations in sphalerite from mineral deposits. 

Geological Society of America, Abstracts with Program, 36(5), 27–28. Online at 

http://gsa.confex.com/gsa/2004AM/finalprogram/abstract_79382.htm. 

Pickardt, J., B. Reuter, E. Riedel, and J. Sochtig (1975), On the formation of FeSe2 single 

crystals by chemical transport reactions. Journal of Solid State Chemistry, 15, 366–368. 

Pitman. A.L., M. Pourbaix, and N. de Zoubov (1957), Potential-pH diagram of the antimonywater system: Its applications to properties of the metal, its compounds, its corrosion, and 

antimony electrodes. Journal of the Electrochemical Society, 104(10), 594–600.

Pitman, J.K., M.B. Goldhaber, and C. Spoetl (1997), Regional diagenetic patterns in the St. Peter 

Sandstone: Implications for brine migration in the Illinois Basin. Evolution of Sedimentary 

Basins—Illinois Basin, U.S. Geological Survey Bulletin 2094-A, pp. A1–A17. 

Plummer, L., J. Busby, R. Lee, and B. Hanshaw (1990), Geochemical modeling of the Madison 

Aquifer in parts of Montana, Wyoming, and South Dakota. Water Resour. Res., 26(9), 1981–

2014. 

Plummer, L.N., and E. Busenberg (1982), The solubilities of calcite, aragonite, and vaterite in 

CO2-H2O solutions between 0 and 90oC and an evaluation of the aqueous model of the 

system CaCO3-CO2-H2O. Geochim. Cosmochim. Acta, 46, 1011–1040. 

Pokrovski, G., R. Gout, J. Schott, A. Zotov, and J. Harrichoury (1996), Thermodynamic and 

stoichiometry of As (III) hydroxide complexes at hydrothermal conditions. Geochim. 

Cosmochim. Acta, 60, 737–749. 

Pokrovski, G.S., S. Kara, and J. Roux (2002), Stability and solubility of arsenopyrite, FeAsS, in 

crustal fluids. Geochim. et Cosmochim. Acta, 66(13), 2361–2378. 
 

345 of 352

Preis, W. and H. Gamsjager (2002), Critical evaluation of solubility data: Enthalpy of formation 

of siderite. Phys. Chem. Chem. Phys., 4, 4014–4019. 

Pruess, K., C. Oldenburg, and G. Moridis (1999), TOUGH2 User’s Guide, Version 2.0, LBL43134, Lawrence Berkeley National Laboratory, Berkeley, CA. 

Reardon, E.J. and R.D. Beckie (1987), Modelling chemical equilibria of acid mine drainage: The 

FeSO4-H2SO4-H2O system. Geochim. et Cosmochim. Acta, 51, 2355–2368. 

Reich, M. and U. Becker (2006), First-principles calculations of the thermodynamic mixing 

properties of arsenic incorporation into pyrite and marcasite. Chem. Geol., 225, 278–290. 

Reich, M., S.E. Kesler, S. Utsunomiya, C.S. Palenik, S.L. Chryssoulis, and R.C. Ewing (2005), 

Solubility of gold in arsenian pyrite. Geochimica et Cosmochimica Acta, 69(11), 2781–2796. 

Rickard, D. (2006), The solubility of FeS. Geochimica et Cosmochimica Acta, 70, 5779–5789. 

Rickard, D. and G.W. Luther III. (2006), Metal sulfide complexes and clusters. Chapter 8 in 

Reviews in Mineralogy and Geochemistry (David J. Vaughan, Ed.), p. 421-504. The 

Mineralogical Society of America, Chantilly, Va, USA. 

Rieder, M., J.C. Crelling, O. Sustai, M. Drabek, Z. Weiss, and M. Klementova (2007), Arsenic in 

iron disulfides in a brown coal from the North Bohemian Basin, Czech Republic. 

International Journal of Coal Geology, 71, 115–121. 

Robie, R.A. (1967), Selected x-ray crystallographic data, molar volumes, and densities of 

minerals and related substances. U.S.G.S. TIC: 248724 (1248, p. 87), Denver, CO. 

Robie, R.A. and B.S. Hemingway (1995), Thermodynamic properties of minerals and related 

substances at 298.15 K and 1 bar (105 pascals) pressure and at higher temperatures. U.S. 

Geol. Surv. Bull. 2131, 461 p. 

Rock, P.A., G.K. Mandell, W.H. Casey, and E.M. Walling (2001), Gibbs energy of formation of 

dolomite from electrochemical cell measurements and theoretical calculations. Am. J. Sci., 

301, 103–111. 

Rojo, J.M., J.L. Mesa, J.L. Pizarro, L. Lezama, M.I. Arriortua, and T. Rojo (1996), 

Spectroscopic and magnetic study of the (Mg,M)3(AsO4)2*8H2O (M = Ni2+, Co2+) arsenates. 

Materials Research Bulletin, 31, 925–934. 

Rozan, T.F., Luther, G.W. III, Ridge, D. and Robinson, S. (2003). Determination of Pb 

complexation in oxic and sulfidic waters using pseudovoltammetry. Environ. Sci. Technol. 

2003, 37, 3845-3852. 

Ruaya, J.R. and T.M. Seward (1986), The stability of chlorozinc (11) complexes in hydrothermal 

solutions up to 350oC. Geochimica Cosmochimica Acta, 50, 651–662. 

Ryser, A.L., D.G. Strawn, M.A. Marcus, J.L. Johnson-Maynard, M.E. Gunter, and G. Möller 

(2005), Micro-spectroscopic investigation of selenium-bearing minerals from the Western 

US Phosphate Resource Area. Geochemical Transactions, 6, 1–11. 

Sacks, L.A. and A.B. Tihansky (1996), Geochemical and isotopic composition of ground water, 

with emphasis on sources of sulfate, in the Upper Floridan Aquifer and Intermediate Aquifer 

System in Southwest Florida. U.S. Geological Survey Water-Resources Investigations Report 

96-4146, 67 p. 
 

346 of 352

Stanton, M.R., P.A. Gemery-Hill, W.C.S. III and C.D. Taylor (2008), Rates of zinc and trace 

metal release from dissolving sphalerite at pH 2.0–4.0. Applied Geochemistry, 23(136-147). 

Sato, M. (1992), Persistency-field Eh-pH diagrams for sulfides and their application to supergene 

oxidation and enrichment of sulfide ore bodies. Geochimica et Cosmochimica Acta, 56(8), 

3133–3156. 

Savage, K.S., T.N. Tingle, P.A. ODay, G.A. Waychunas, and D.K. Bird (2000), Arsenic 

speciation in pyrite and secondary weathering phases, Mother Lode gold district, Tuolumne 

County, California. Appl. Geochem., 15, 1219–1244. 

Schaefer, S.C. and N.A. Gokcen (1982), Electrochemical determination of the thermodynamic 

properties of sphalerite, ZnS(beta). High Temperature Science, 15, 225–237. 

Schiferl, D. and C.S. Barrett (1969), The crystal structure of arsenic at 4.2, 78 and 299 K. 

Journal of Applied Crystallography, 2, 30–36. 

Schoonen, M.A.A. and Barnes, H.L. (1988). An approximation of the second dissociation 

constant for H2S. Geochim. Cosmochim. Acta, vol. 52, p. 649-654. 

Schwarzenbach, G. and M. Widmer (1963), Die Löslichkeit von Metallsulfiden: I. Schwarzes 

Quecksilbersulfid. Helv. Chim. Acta, 46, 2613–2628. 

Seal, R.R., R.A. Robie, P.B. Barton, and B.S. Hemingway (1992), Superambient heat capacities 

of synthetic stibnite, berthierite, and chalcostibnite: Revised thermodynamic properties and 

implications for phase equilibria. Econ. Geol., 87, 1911–1918. 

Seby, F., M. Potin-Gautier, E. Giffaut, G. Borge, and O.F.X. Donard (2001), A critical review of 

thermodynamic data for selenium species at 25oC. Chemical Geology, 171, 173–194. 

Selim, H. M. and Amacher, M. C. (1996). Reactivity and transport of heavy metal in soil. Lewis 

Publishers, 201 pp. 

Seward, T.M. (1984), The formation of lead (II) chloride complexes to 300°C: A 

spectrophotometric study. Geochimica Cosmochimica Acta, 48, 121–134. 

Shaw, D.M., J. Dostal, and R.R. Keays (1985), Additional estimates of continental surface 

Precambrian shield composition in Canada. Geochim. Cosmochim. Acta, 40, 73–83. 

Shea, D. and G.R. Helz (1989), Solubility product constants of covellite and a poorly crystalline 

copper sulfide precipitate at 298 K. Geochimica et Cosmochimica Acta, 53, 229–236. 

Shelton, R.A.J. (1970), PbCl2, SnCl2 and CuCl from emf measurements on galvanic cells with a 

solid electrolyte. Trans. Inst. Min. and Met., C, 79, C215–C220. 

Shipton, Z.K., J.P. Evans, B. Dockrill, J. Heath, A. Williams, D. Kirchner, and P.T. Kolesar 

(2004), Natural leaking CO2-charged systems as analogues for failed geologic sequestration 

reservoirs. In: Geological Storage of Carbon Dioxide, S.J. Baines and R.H. Worden, eds., 

Geological Society, London, Special Publications, 233, pp. 43–58. 

Shock, E.L. and H.C. Helgeson (1988), Calculation of the thermodynamic and transport 

properties of aqueous species at high pressures and temperatures: Correlation algorithms for 

ionic species and equation of state predictions to 5 kb and 1000°C. Geoch. Cosmochim. Acta, 

52, 2009–2036. 
 

347 of 352

Shock, E.L., H.C. Helgeson, and D.A. Sverjensky (1989), Calculation of the thermodynamic and 

transport properties of aqueous species at high pressures and temperatures: Standard partial 

molal properties of inorganic neutral species. Geoch. Cosmochim. Acta, 53, 2157–2183. 

Shock, E.L., E.H. Oelkers, J.W. Johnson, D.A. Sverjensky, and H.C. Helgeson (1992), 

Calculation of the thermodynamic and transport properties of aqueous species at high 

pressures and temperatures: Effective electrostatic radii to 1000C and 5 kb. J. Chem. Soc. 

London Faraday Trans., 88, 803–826. 

Shock E.L., D.C. Sassani, M. Willis, and D.A. Sverjensky (1997), Inorganic species in geologic 

fluids: Correlations among standard partial molal thermodynamic properties of aqueous ions 

and hydroxide complexes. Geoch. Cosmochim. Acta, 61, 907–950. 

Shpanov E. P., G. A. Sidorenko, and T. I. Stolyarova (1970), Akdalaite, a new hydrous 

modification of alumina: Zap. Vses. Mineral. Obshch., 99, 333-339 [in Russian]. 

Simon, G., H. Huang, J.E. Penner-Hahn, S.E. Kesler, and L.-S. Kao (1999a), Oxidation state of 

gold and arsenic in gold-bearing arsenian pyrite. Am. Mineral., 84, 1071– 1079. 

Simon, G., S.E. Kesler, and S. Chryssoulis (1999b), Geochemistry and textures of gold-bearing 

arsenian pyrite, Twin Creeks, Nevada; implications for deposition of gold in Carlin-type 

deposits. Econ. Geol., 94, 405–421. 

Simon, L., M. François, P. Refait, G. Renaudin, M. Lelaurain, and J.-M.R. Génin (2003), 

Structure of the Fe(II-III) layered double hydroxysulphate green rust two from Rietveld 

analysis. Solid State Sciences, 5, 327–334. 

Singer, P.C. and W. Stumm (1968), Oxygenation of ferrous iron: The rate determining step in the 

formation of acidic mine drainage. Federal Water Pollution Control Administration 

Research Series Report. NTIS Report No. PB-189-233. 

Singer, P.C. and W. Stumm (1970), Acid mine drainage: The rate-determining step. Science, 

167, 1121–1123. 

Skinner, B.J., R.C. Erd, and F.S. Grimaldi (1964), Greigite, the thio-spinel of iron; a new 

mineral. American Mineralogist, 49, 543–555. 

Smedley, P. L. and W. M. Edmunds (2002), Redox patterns and trace-element behavior in the 

East Midlands Triassic Sandstone Aquifer, U.K. Ground Water, 40(1), 44–58. 

Smedley, P.L. (2003), Arsenic in groundwater – south and east Asia. Chapter 7 in Arsenic in 

Ground Water, A.H. Welch and K.G. Stollenwerk, eds., Kluwer Academic Publishers, 

Boston, pp. . 

Smith, E.H., W. Lu, T. Vengris, and R. Binkiene (1996), Sorption of heavy metals by lithuannian 

glauconite. Water Research, 30(12), 2883–2892. 

Smith, F.G. (1963), Physical Geochemistry. Addison-Wesley Publishing Company, Inc., 

Reading, Massachusetts, 624 p 

Solans-Huguet, J. and M. Font-Altaba (1967) Estudio de un modelo teoretico de la formacion del 

mineral cotunnita. Bol. R. Soc. Espanola Hist. Nat. (Geol), 65, 51–56. 

Sonnenthal, E., A. Ito, N. Spycher, M. Yui, J. Apps, Y. Sugita, M. Conrad and S. Kawakami 

(2005), Approaches to modeling coupled thermal, hydrological, and chemical processes in 

the Drift Scale Heater Test at Yucca Mountain. Int. J. Rock Mech. Min. Sci., 42: 6987–719. 
 

348 of 352

Steefel, C.I. and A.C. Lasaga (1994), A coupled model for transport of multiple chemical species 

and kinetic precipitation/dissolution reactions with applications to reactive flow in a singlephase hydrothermal system. Am. J. Sci, 294, 529–592. 

Steefel, C.I. (2001), GIMRT, Version 1.2: Software for Modeling Multicomponent, 

Multidimensional Reactive Transport: Users Guide. Lawrence Livermore National 

Laboratory, Livermore, California. 

Stefansson, A., S. Arnorsson, and A.E. Sveinbjornsdottir (2005), Redox reactions and potentials 

in natural waters at disequilibrium. Chemical Geology, 221, 289–311. 

Ste-Marie, J., A.E. Torma, and A.O. Gübeli (1964), The stability of thiocomplexes and solubility 

products of metal sulphides: I. Cadmium sulphide. Can. J. Chem., 42(3), 662–668. 

Stevenson, R.K. and R.F. Martin (1986). Implications of the presence of amazonite in the Broken 

Hill and Geco metamorphosed sulfide deposits. Canadian Mineralogist, 24, 729–745. 

Stubbles, J.R. and C.E. Birchenall (1959), Redetermination of the lead sulfide equilibrium 

between 585 and 920oC. Trans. Met. Soc. AIME, 215, 535–539. 

Suzuki, Y., S.D. Kelly, K.M. Kemner, and J.F. Banfield (2005), Direct microbial reduction and 

subsequent preservation of uranium in natural near-surface sediment. Applied and 

Environmental Microbiology, Apr. 2005, 1790–1797. 

Svensson, C. (1974), The crystal structure of orthorhombic antimony trioxide, Sb2O3. Acta 

Crystallographica, B30, 458–461. 

Svensson, C. (1975), Refinement of the crystal structure of cubic antiomony trioxide, Sb2O3. 

Acta Crystallographica, B31, 2016–2018. 

Sverjensky, D.A., E. Shock, and H.C. Helgeson (1997), Prediction of the thermodynamic 

properties of aqueous metal complexes to 1000 degrees C and 5 kb. Geoch. Cosmochim. 

Acta, 61, 1359–1412. 

Szalay, A. (1964), Cation exchange properties of humic acids and their importance in the 

geochemical enrichment of UO2

++ and other cations. Geochimica et Cosmochimira Acta, 28, 

1605-1614. 

Tagirov, B. and J. Schott (2001), Aluminum species in crustal fluids revisited. Geochim. 

Cosmochim. Acta, 65(21), 3965–3992. 

Tanger J.C. and H.C. Helgeson (1988), Calculation of the thermodynamic and transport 

properties of aqueous species at high pressures and temperatures: Revised equations of state 

for the standard partial molal properties of ions and electrolytes. Amer. J. Sci., 288. 

Taylor, P. and V.J. Lopata (1984), Stability and solubility relationships between some solids in 

the system PbO-CO2-H2O. Can. J. Chem., 62, 395–402. 

Taylor, S.R. and S.M. McLennan (1985), The Continental Crust: Its Composition and Evolution. 

Blackwell Sci. Publ., Oxford, 330 pp. 

Tewalt, S.J., L.J. Bragg, and R.B. Finkelman (2001), Mercury in U.S. coal—Abundance, 

distribution, and modes of occurrence. USGS Fact Sheet FS-095-01, U.S. Geological Survey, 

U.S. Department of the Interior, 3 p. 
 

349 of 352

Theberge, S. and G.W. Luther (1997), Determination of the electrochemical properties of a 

soluble aqueous FeS species present in sulfide solutions. Aquatic Geochem. 3, 191–211. 

Thiel, G.A. (1935), Sedimentary and petrographic analysis of the St. Peter Sandstone. Geological 

Society of America Bulletin, 46, 559–614. 

Thornberg, K. and N. Sahai, (2004), Arsenic occurrence, mobility, and retardation in sandstone 

and dolomite formations of the Fox River valley, Eastern Wisconsin. Environ. Sci. Technol., 

38, 5087–5094. 

Tissot, B.P. and D.H. Welte (1978), Petroleum Formation and Occurrence: A New Approach to 

Oil and Gas Exploration. Springer Verlag, New York, 538 p. 

Tilley, D.B. and Eggleton, R.A. (1994), Tohdite (5A12O3.H2O) in bauxites from Northern 

Australia. Clays and Clay Minerals, Vol. 42, No. 4, 485-488. 

Tolkachev, S. S., E.V. Stroganov., I. Kozina (1958), The structure of lead oxide monohydrate. 

Vestn. Leningr. Univ. Ser. Fizik. Khim.,16, 134-139. [In Russian] 

Toptygina, G.M., A.A. Soldatov, and V.I. Evdokimov (1984), The reaction of lead chloride with 

calcium hydroxide in aqueous solution. Russian Journal of Inorganic Chemistry, 29(11) 

1580–1582. [Translated from Zhurnal Neorganicheskoi Khimii, 29, 2757-2759 (1984)]. 

Tossell, J.A. (1999), Theoretical studies on the formation of mercury complexes in solution and 

the dissolution and reactions of cinnabar. American Mineralogist, 84, 877–883. 

Tridot, G., J.-C. Boivan, and D. Thomas, (1969), Contribution a l’etude du systeme PbO-PbSO4. 

J. Thermal Analysis, 1(1), 35–46 Doi:10.1007/BF01911244. 

Tronc, E., C. Chaneac, and J. P. Jolivet (1998), Structural and magnetic characterization of eFe2O3. Journal of Solid State Chemistry, 139, 93–104. 

Tucker, M.E. (1991), Sedimentary Petrology: An Introduction to the Origin of Sedimentary 

Rocks. Blackwell Scientific Publications, Oxford. 2nd Edition, 260 p. 

Turekian, K.K. (1970), in McGraw-Hill Encyclopedia of Science and Technology, 4, 627. 

Uhler, A.D. and G.R. Helz (1984), Solubility product of galena at 298 K: A possible explanation 

for apparent supersaturation in nature. Geochimica et Cosmochimica Acta, 48, 1155–60. 

USGS (2008), National Geochemical Survey database. http://tin.er.usgs.gov/geochem/ 

USGS, (2001), Mercury in U.S. Coal -- Abundance, Distribution, and Modes of Occurrence. 

U.S. Geological Survey Fact Sheet FS-095-01, 6 p. 

Utsunomiya, S., S.C. Peters, J.D. Blum, and R.C. Ewing (2003), Nanoscale mineralogy of 

arsenic in a region of New Hampshire with elevated As-concentrations in the groundwater. 

Am. Miner. 88, 1844–1852. 

Vaughan, D.J. and J.R. Craig (1978), Mineral Chemistry of Metal Sulfides. Cambridge Earth 

Science Series. Harland, W.B., ed. New York: Cambridge University Press. 493 p. 

Vikre, Peter G. (2005), Se-rich precious metal deposits and the significance of Se in sinter, 

Northern Great Basin (Abstract). Paper No. 235-8, The Geological Society of America 2005 

Salt Lake City Annual Meeting (October 16–19, 2005). 
 

350 of 352

Wagman, D.D., W.H. Evans, V.B. Parker, I. Halow, S.M. Bailey, and R.H. Schumm (1968), 

Selected values of chemical thermodynamic properties. Nat. Bur. Stand. (U.S.). Tech. Note, 

270–3, 264 p. 

Wagman, D.D., W.H. Evans, V.B. Parker, R.H. Schumm, I. Halow, S.M. Bailey, K.L. Churney, 

and R.L. Nuttall (1982), The NBS Tables of Chemical and Thermodynamic Properties. Jour. 

Phys. Chem. Ref. Data, v. 11, Supplement no. 2, 392 p. 

Walker, F.P., M.E. Schreiber and J.D. Rimstidt, 2006. Kinetics of arsenopyrite oxidative 

dissolution by oxygen. Geochimica et Cosmochimica Acta, 70: 1668-1676. 

Wang, S. and P.R. Jaffe (2004), Dissolution of a mineral phase in potable aquifers due to CO2

releases from deep formations; effect of dissolution kinetics. Energy Conversion and 

Management, 45, 2833–2848. 

Wänke, H., G. Dreibus, and E. Jagoutz (1984), Mantle chemistry and accretion history of the 

Earth. In: Archean Geochemistry, A. Kröner, G.N. Hanson, A.M. Goodwin, eds., pp. l–24, 

Springer-Verlag, Berlin. 

Weast, R.C., Astle, M.J. and W.H. Beyer (Eds). (1984), CRC Handbook of Chemistry and 

Physics CRC Press, Inc., Boca Raton, Florida. 

Weaver, B.L. and J. Tamey (1984), Major and trace element composition of the continental 

lithosphere. In Physics and Chemistry of the Earth, 15, H.N. Pollack, V.R. Murthy, eds., pp. 

39–68, Pergamon, Oxford. 

White, D., D.C. McPhail, and D. Lawie (2004), Fractionation of Zn and Cd in soils proximal and 

distal to sulphide mineralistion. In: Regolith 2004, I.C. Roach, ed., , pp. 394–397, CRC for 

Landscape Environments and Mineral Exploration, Bentley, Western Australia, Australia, 

ISBN 0-9756895-1-7 (CD-ROM). 

Whitfield, M. and D.R. Turner (1980), The theoretical studies of the chemical speciation of lead 

in seawater. In: Lead in the Marine Environment, M. Branica and Z. Konrad eds., pp. 109–

148, Pergamon, New York. 

Wiegers, G A. (1971), The crystal structure of the low-temperature form of silver selenide. 

American Mineralogist, 56(1971), 1882–1888. 

Wieser, M. E. (2006), Atomic weights of the elements 2005, (IUPAC Technical Report). Pure 

Appl. Chem., 78(11), 2051–2066. 

Wise, W.S. and S.E. Loh (1976), Equilibria and origin of minerals in the system Al2O3

-

AlPO4-

H2O. American Mineralogist, 6I, 409–413. 

Williams Jones, A.E. and C. Normand (1997), Controls of mineral parageneses in the system FeSb-S-O. Economic Geology, 92(3), 308–324. 

Wolfe, C.W. (1940), Classification of minerals of the type A3(XO4)2·nH2O (concluded) 

American Mineralogist, 25(12), 787–809. 

Wolery, T.J.(1992), EQ3NR, A computer program for geochemical aqueous speciation-solubility 

calculations: Theoretical manual, user’s guide, and related documentation (Version 7.0). 

Lawrence Livermore National Lab. UCRL-MA-110662 PT III. 

Wolery, T.J. (1993), EQ3/6, A software package for geochemical modelling of aqueous systems 

(Version 7.2). Lawrence Livermore Nat. Lab. UCRL-MA 110662. 
 

351 of 352

Wood, R.H. (1958), The second ionization constant of hydrogen selenide. J. Am. Chem. Soc., 80, 

1559–1562. 

Wright, H.D., W.M. Barnard, and J.R. Halbig (1965), Solid solution in the systems ZnS-ZnSe 

and PbS-PbSe at 300°C. and above. American Mineralogist, 50, 1802–1815. 

Wyckoff, R.W.G. (1963), [Antimony] In: Crystal Structures 1, pp. 7–83. Second edition. 

Interscience Publishers, New York, New York 

Wyckoff, R.W.G. (1963), [Clausthalite]. In: Crystal Structures, 1, pp. 85–237, Second edition. 

Interscience Publishers, New York, New York. 

Wyckoff, R.W.G. (1963), [Greenockite cell parameters.]. In: Crystal Structures, 1, pp. 85–237, 

Second edition. Interscience Publishers, New York, New York 

Wyckoff, R.W.G. (1963), [Lepidocrocite structure]. In: Crystal Structures, 1, 290–295 Second 

edition. Interscience Publishers, New York, New York. 

Wyckoff, R.W.G. (1963), [Uraninite cell parameters.]. In Crystal Structures, 1, 239-444, Second 

edition. Interscience Publishers, New York, New York. 

Xiong, Y. (2003), Predicted equilibrium constants for solid and aqueous selenium species to 

300°C: Applications to selenium-rich mineral deposits. Ore Geology Reviews, 23, 259–276. 

Xu, T., J.A. Apps, and K. Pruess (2003), Reactive geochemical transport simulation to study 

mineral trapping for CO2 disposal in deep arenaceous formations. J. Geophys. Res., 108(B2), 

2071-2084. 

Xu, T., E. Sonnenthal, N. Spycher, and K. Pruess (2006), TOUGHREACT: A simulation 

program for non-isothermal multiphase reactive geochemical transport in variably saturated 

geologic media. Computers and Geosciences, 32, 145–165. 

Xu, T., J. Apps, and J. Birkholzer (2007), Contamination of groundwater by hazardous inorganic 

chemical constituents through induced acidification due to CO2 leakage from a storage 

formation. Abstract in: Proceedings of the 6th Annual Conference on Carbon Capture and 

Sequestration, Pittsburgh, PA, May 2007. 

Xu, T., J.A. Apps, K. Pruess and H. Yamamoto, (2007). Numerical modeling of injection and 

mineral trapping of CO2 with H2S and SO2 in a sandstone formation. Chemical Geology, 

242: 319-346. 

Xu, Y., G.P. Zhou, and X.F. Zheng (2007), Redetermination of iron(III) arsenate dihydrate. Acta 

Crystallographica, E63, i67–i69. 

Yakovleva, V.A., E.V. Belogub, and K.A. Novoselov (2003), Supergene iron sulpho-selenides 

from the Zapadno-Ozernoe copper-zinc massive sulphide deposit, South Urals, Russia: A 

new solid- solution series between pyrite FeS2 and dzharkenite FeSe2. Mineralogical 

Magazine, 67(2). 

Yang, H., R. Lu, R.T. Downs, and G. Costin (2006), Goethite, alpha-FeO(OH), from singlecrystal data. Acta Crystallographica, E62, i250–i252. 

Yongshan, D., J.M. Hughes, and P.B. Moore (1991), The crystal structures of mimetite and 

clinomimetite, Pb5(AsO4)3Cl. The Canadian Mineralogist, 29, 369–376. 
 

352 of 352

Yu, Y., Y. Zhu, Z. Gao, C.H. Gammons and D. Li (2007), Rates of arsenopyrite oxidation by 

oxygen and Fe(III) at pH 1.8-12.6 and 15-45 °C. Environ. Sci. Technol., 41: 6460-6464. 

Zacharias, J., J. Fryda, B. Paterova, and M. Mihaljevic (2004), Arsenopyrite and As-bearing 

pyrite from the Roudny deposit, Bohemian Massif. Mineralogical Magazine, 68(1), 31-46. 

Zerai B., B.Z. Saylor, and G. Matisoff (2006), Computer simulation of CO2 trapped through 

mineral precipitation in the Rose Run Sandstone, Ohio. Appl. Geochem., 21, 223–240. 

Zhang, J.-Z. and Millero, F.J. (1994), Investigation of metal sulfide complexes in sea water using 

cathodic stripping square wave voltammetry. Analytica Chimica Acta, 284, 497-504. 

Zhang, S., J. Li, Y. Wang and G. Hu (2003), Dissolution kinetics of galena in acid NaCl 

solutions at 25—75 οC. Applied Geochemistry, 19: 835-841. 

Zhu, Y.N., X.H. Zhang, Q.L. Xie, D.Q. Wang, and Q.W. Cheng (2006), Solubility and stability 

of calcium arsenates at 25oC. Water, Air and Soil Pollution, 169, 221–238. 

Zotov, A.V., N.D. Shikina, and N.N. Akinfiev (2003), Thermodynamic properties of Sb(III) 

hydroxide complex Sb(OH)3(aq) at hydrothermal conditions. Geoch. Cosmochim. Acta, 67, 

1821–1836. 
 

 

Research Project on CO2 Geological Storage and 

Groundwater Resources 

Water Quality Effects Caused by CO2 Intrusion into 

Shallow Groundwater 

Appendices A-F 
Page A1 of 10 

Appendix A: 

Lists of Analytical Methods for Groundwater Analysis 

Table A1. Summary of Groundwater Analyses Data Available for Trace Elements (see list of analytical methods in 

Table A2) 

(a) Summary of Arsenic (As) Analyses 

Analytical 

Method 

Code 

PCode Total No. of 

Analyses 

Detection 

Limit 

(mg/L) 

No. below 

Detn Limit 

No. at or 

above 

Detn Limit

Percentage at or 

above Detn. Limit 

(%) 

Notes 

GF042 01000 135 0.001 124 11 8.15 

GF044 01002 2 0.001 1 1 50.00 

GF073 01002 9 0.005 1 8 88.89 

PL120 01002 2 0.001 1 1 50.00 

PLA06 01000 5 0.002 4 1 20.00 

GF085 01000 652 0.001 

0.002 

0.004 

175 

91 

1 

385 

59.05 

GF096 01002 157 0.002 

0.003 

0.004 

0.008 

60 

2 

2 

1 

92 

58.60 

GF031 01002 69 0.001 44 25 36.23 

HY010 01000 5522 0.001 

0.005 

0.01 

2309 

1 

9 

3203 

58.00 

HY013 01002 137 0.001 81 56 40.88 

PLA07 01002 117 0.002 

0.004 

33 

66 

99 

84.62 

PLM10 01000 76 0.00012 6 70 92.11 

PLM27 01000 174 0.002 

0.001 

0.01 

104 

24 

2 

144 

82.76 

PLM40 01000 2782 0.0002 

0.0003 

0.0004 

0.0009 

201 

72 

1 

87 

2421 

87.02 

Unknown 11610 >0.1 

0.01-0.1 

0.01 

0.005-0.01 

0.005 

0.004 

0.003-0.004 

0.003 

0.002 

0.001 

0.000001-

0.001 

0 

82 

131 

3366 

11 

593 

36 

1 

10 

94 

2586 

149 

14 

3688 

31.77 

40 presence verified but 

not quantified. 

788 analyzed but not 

detected 

1 greater than 1.e6 

mg/L 
Page A2 of 10 

(b) Summary of Barium (Ba) Analyses 

Analytical 

Method 

Code 

PCode Total No. of 

Analyses 

Detection 

Limit 

(mg/L) 

No. below 

Detn Limit 

No. at or 

above 

Detn Limit 

Percentage at or 

above Detn. Limit 

(%) 

Notes 

AA003 01005 117 0.10 70 47 40.17 

GF046 01005 16 - 0 16 100.00 

PLA06 01005 24 0.0005 1 23 95.83 

PLM27 01005 177 0.1 18 159 89.83 

PLA11 01005 5178 0.0009 

0.001 

0.002 

0.003 

0.006 

0.1 

6 

9 

100 

3 

1 

6 

5053 

97.59 

PLM43 01005 1252 0.0002 

0.001 

2 

29 

1206 

96.33 

15 Presence verified but 

not quantified 

PLM47 01007 87 0.0002 1 86 98.85 

GF033 01007 18 - 0 18 100.00 

PLA15 01007 13 - 0 13 100.00 

PLA07 01007 120 - 0 120 100.00 

PLA06 01007 24 - 0 24 100.00 

unknown 7337 0.0001 

0.0002 

0.001 

0.002 

0.005 

0.01 

0.025 

0.03 

0.04 

0.05 

0.06 

0.07 

0.1 

0.2 

0.3 

0.4 

0.5 

10 

2875 4462 

60.82 
Page A3 of 10 

(c) Summary of Cadmium (Cd) Analyses 

Analytical 

Method 

Code 

PCode Total No. of 

Analyses 

Detection 

Limit 

(mg/L) 

No. below 

Detn Limit 

No. at or 

above 

Detn Limit 

Percentage at or 

above Detn. Limit 

(%) 

Notes 

AA005 01025 39 0.01 38 1 2.56 

CX002 01025 39 0.001 

0.004 

0.01 

31 

1 

1 

6 

15.38 

GF086 01025 2421 0.00001 

0.001 

1 

2405 

15 

0.62 

CX013 01027 27 0.001 19 8 29.63 

GF097 01027 67 0.001 67 0 0.00 

PLA06 01025 23 0.0005 22 1 4.35 

PLA07 01027 118 0.0005 114 4 3.39 

PLA11 01025 2403 0.001 

0.002 

0.003 

0.005 

0.008 

0.009 

0.01 

1964 

12 

18 

7 

49 

1 

4 

348 

14.48 

PLM27 01025 177 0.001 

0.002 

31 

145 

1 

0.56 

PLM43 01025 3290 0.00004 

0.001 

others 

1385 

1093 

15 

797 

24.22 

PLM47 01027 64 0.00004 53 11 17.19 

Unknown 01025 or 

01027 

12201 0 

0-0.0002 

0.0002 

0.0005 

0.0006 

0.001 

0.001-0.005 

0.005 

0.005-0.01 

0.01 

0.02 

225 

89 

229 

1339 

117 

2653 

2628 

484 

11 

940 

1095 

319 

2.6 

1118 Analyzed for but 

not detected 

953 Presence verified 

but not quantified 

(d) Summary of Mercury (Hg) Analyses 

Analytical 

Method 

Code 

PCode Total No. of 

Analyses 

Detection 

Limit 

(mg/L) 

No. below 

Detn Limit 

No. at or 

above 

Detn Limit 

Percentage at or 

above Detn. Limit 

(%) 

Notes 

CV013 71890 2534 0.0001 

0.0002 

0.0003 

0.001 

2210 

14 

1 

6 

303 

11.96 

CV014 71890 112 0.0001 

0.0002 

78 

24 

10 

8.93 

CV016 71900 111 0.0001 

0.0002 

94 

1 

16 

14.41 

CV017 71900 58 0.0001 58 0 0.00 

CV018 71900 69 0.0001 

0.0002 

36 

25 

8 

11.59 
Page A4 of 10 

(e) Summary of Lead (Pb) Analyses 

Analytical 

Method 

Code 

PCode Total No. of 

Analyses 

Detection 

Limit 

(mg/L) 

No. below 

Detn Limit 

No. at or 

above 

Detn Limit 

Percentage at or 

above Detn. Limit 

(%) 

Notes 

AA019 01049 74 0.1 71 3 4.05 

CX007 01049 501 0.001 

0.004 

0.005 

0.01 

300 

1 

54 

11 

135 

26.95 

CX017 01049 40 0.005 22 18 45.00 

GF031 01051 47 0.001 29 18 38.30 

GF056 01051 1 0.005 1 0 0.00 

GF078 01049 1 0.005 1 0 0.00 

GF089 01049 2876 0.001 

0.002 

0.1 

2531 

1 

1 

341 

11.86 

2 empty values 

GF100 01051 94 0.001 

0.005 

36 

1 

57 

60.64 

PLA06 01049 23 1.001 

0.002 

16 

5 

2 

8.70 

PLA07 01051 123 0.001 

0.002 

39 

76 

8 

6.50 

PLA11 01049 1657 0.001 

0.01 

0.02 

0.03 

0.05 

0.1 

1 

1368 

9 

15 

7 

40 

215 

12.98 

2 empty values 

PLM43 01049 1970 0.0001 

0.0001-

0.001 

0.001 

0.002 

0.004 

285 

6 

701 

7 

1 

970 

49.24 

PLM48 01051 100 0.00006 

0.001 

6 

12 

82 

82.00 

Unknown 12374 0 

0.000001 

0.00001 

0.00001-

0.0001 

0.0001-

0.001 

0.001 

0.001-0.01 

0.01 

0.01-0.1 

0.1 

0.1-1 

>1 

187 

55 

4 

24 

81 

417 

3932 (3337 

of them are 

0.005) 

3466 

499 

271 

18 

11 

438 

3.54 

971 Analyzed for but 

not detected 

2000 Presence verified 

but not quantified 
Page A5 of 10 

(f) Summary of Antimony (Sb) Analyses 

Analytical 

Method 

Code 

PCode Total No. of 

Analyses 

Detection 

Limit 

(mg/L) 

No. below 

Detn Limit 

No. at or 

above 

Detn Limit 

Percentage at or 

above Detn. Limit 

(%) 

Notes 

GF032 01095 1 0.001 1 0 0.00 

GF040 01097 115 0.001 115 0 0.00 

PLM48 01097 54 0.0002 

0.0006 

0.0009 

49 5 

9.26 

HY009 01095 

01097 

516 0.001 432 84 

16.28 

PLM43 - 1274 0.00005 

0.0001 

0.0002 

0.0003 

0.0004 

0.0006 

0.0008 

0.001 

0.002 

>=0.003 

105 

1 

219 

148 

1 

1 

2 

555 

7 

2 

233 

18.29 

Trivial No. of other 

misc. detn limits 

Unknown 01095 790 Misc: 

0.00005-0.3 

761 29 

3.67 

(g) Summary of Selenium (Se) Analyses 

Analytical 

Method 

Code 

PCode Detection 

Limit 

(mg/L) 

No. below 

Detn Limit 

No. at or 

above 

Detn Limit 

Percentage at or 

above Detn. Limit 

(%) 

Notes 

GF060 01145 0.001 16 6 27.27 

PLM10 01145 0.0008 17 52 75.36 

GF091 01145 0.001 

0.002 

0.006 

0.014 

297 

41 

6 

4 

80 

18.69 

PLM27 01145 0.01 153 21 12.07 

HY011 01145 0.001 

0.002 

0.003 

0.005 

0.01 

3240 

61 

1 

3 

10 

752 

18.49 

PLM40 01145 0.0003 

0.0004 

0.0005 

0.0007 

0.002 

365 

250 

230 

160 

1 

1411 

58.38 

Unknown 01145 0 

<0.001 

0.001 

0.001-0.01 

0.01 

0.01-0.1 

>=0.1 

192 

112 

932 

248 

2170 

53 

53 

1108 

22.76 
Page A6 of 10 

(h) Summary of Uranium (U) Analyses 

Analytical 

Method 

Code 

PCode Total No. of 

Analyses 

Detection 

Limit 

(mg/L) 

No. below 

Detn Limit 

No. at or 

above 

Detn Limit 

Percentage at or 

above Detn. Limit 

(%) 

Notes 

PHOS1 22703 218 0.0004 72 146 66.97 

PHOS2 22703 4 0.09 1 3 75.00 

PHOS3 22703 141 0.00001 10 131 7.09 

PHOS4 22703 1 0.00024 1 0 0.00 

PHOS5 22703 162 0.00 8 149 

4.94 

5 of them are “Presence 

verified but not 

detected” 

PHOS6 22703 61 0.001 44 2 

3.28 

15 of them are 

“Presence verified but 

not detected” 

PLM43 22703 1310 0.004 

0.003 

0.002 

0.001 

0.00016 

0.00007 

0.00004 

0.00002 

1 

1 

4 

315 

1 

1 

31 

43 

913 

69.70 

PLM48 28011 62 - 0 62 100. 

FL001 22703 163 0.001 120 43 26.38 

FL002 75990 122 - 0 122 100.00 

FL003 28011 

28011 

75993 

56 0.001 21 35 

60.00 

FL004 22703 4 - 0 4 100.00 

FL006 22703 59 0.0004 18 41 69.49 

FL007 80020 33 0.00001 1 32 96.97 

Unknown 848 0.00001 

0.0001 

0.0002 

0.0003 

0.0004 

0.0005 

0.0006 

0.0007 

0.0011 

0.007 

0.013 

0.02 

11 

56 

38 

1 

12 

16 

20 

3 

2 

1 

63 

6 

618 

72.88 
Page A7 of 10 

(i) Summary of Zinc (Zn) Analyses 

Analytical 

Method 

Code 

PCode Total No. of 

Analyses 

Detection 

Limit 

(mg/L) 

No. below 

Detn Limit 

No. at or 

above 

Detn Limit 

Percentage at or 

above Detn. Limit 

(%) 

Notes 

AA039 01090 187 0.01 75 112 59.89 

AA071 01092 125 0.01 26 99 79.20 

PLM48 01092 89 0.002 9 80 89.89 

GF084 01090 1 0 1 100.00 

PLA06 01090 83 0.001 

0.002 

0.01 

2 

16 

1 

64 

77.11 

PLA07 01092 141 0.001 

0.002 

4 

14 

123 

87.23 

PLA15 01092 17 0.006 

0.031 

0.04 

6 11 

64.71 

PLM10 01090 11 0.0006 2 9 81.82 

PLM27 01090 175 0.01 120 55 31.43 

PLA11 01090 5407 0.003 

0.004 

0.005 

0.006 

0.007 

0.009 

0.01 

0.014 

0.02 

0.024 

0.06 

0.08 

587 

8 

7 

5 

1 

5 

11 

1 

342 

7 

6 

1 

4426 

81.86 

PLM43 01090 1554 0.0006 

0.001 

0.001-0.01 

0.0101 

4 

87 

7 

1 

1455 

93.63 

Unknown 01090 

01092 

14304 0 

0-0.0001 

0.001 

0.001-0.01 

0.01 

0.02 

0.03 

0.04 

0.05 

0.05-0.1 

0.1 

>0.1 

64 

21 

9 

392 

1378 

1924 

8 

13 

2010 

65 

103 

16 

8301(1225 are 

zeros) 

58.03 
Page A8 of 10 

Table A2. List and Explanation of Analytical Methods from USGS NWIS Database Used in Table A 

Analytical 

Method 

Code 

Short Info Long Info Database Citation Method Number Method 

Owner 

AA003 Barium, wf, direct AAS Barium in filtered water by direct AAS TWRI 5-A1/1989, p. 89 I-1084-85:USGS USGSNWQL 

AA005 Cadmium, wf, direct 

AAS 

Cadmium in filtered water by direct AAS TWRI 5-A1/1989, p. 127 I-1135-85:USGS USGSNWQL 

AA019 Lead, wf, direct AAS Lead in filtered water by direct AAS TWRI 5-A1/1989, p. 247 I-1399-85:USGS USGSNWQL 

AA039 Zinc, wf, direct AAS Zinc in filtered water by direct AAS TWRI 5-A1/1989, p. 507 I-1900-85:USGS USGSNWQL 

AA071 Zinc, wu, USGS digest, 

AAS 

Zinc recoverable from unfiltered water by 

dilute HCl (USGS) digestion and direct 

AAS 

TWRI 5-A1/1989, p. 507 I-3900-85:USGS USGSNWQL 

CV013 Mercury, wf, auto 

CVAAS 

Mercury in filtered water by automatedsequential CVAAS TWRI 5-A1/1989, p. 289 I-2462-85:USGS USGSNWQL 

CV014 Mercury, wf, CV-AFS Mercury in Filtered Water by Cold VaporAtomic Fluorescence Spectrometry WRI 01-4132 I-2464-01:USGS USGSNWQL 

CV016 Mercury, wu, CVAAS 

(NWQL) 

Mercury recoverable from unfiltered water 

by CVAAS 

TWRI 5-A1/1989, p. 285 I-3462-85:USGS USGSNWQL 

CV017 Mercury, wu, CVAAS 

(NWQL) 

Mercury recoverable from unfiltered water 

by CVAAS 

TWRI 5-A1/1989, p. 285 I-3462-85:USGS USGSOCFL 

CV018 Mercury, wu, CV-AFS Mercury in Unfiltered Water by Cold 

Vapor-Atomic Fluorescence Spectrometry 

WRI 01-4132 I-4464-01:USGS USGSNWQL 

CX002 Cadmium, wf, APDC & 

MIBK, AAS 

Cadmium in filtered water by chelationextraction with APDC and MIBK, and 

AAS 

TWRI 5-A1/1989, p. 125 I-1136-85:USGS USGSOCFL 

CX007 Lead, wf, APDC & 

MIBK, AAS 

Lead in filtered water by chelationextraction with APDC and MIBK, and 

AAS 

TWRI 5-A1/1989, p. 245 I-1400-85:USGS USGSOCFL 

CX013 Cadmium, USGS 

digest,extract AAS 

Cadmium recoverable from unfiltered water 

by dilute HCl (USGS) digestion, chelationextraction with APDC and MIBK, and 

AAS 

TWRI 5-A1/1989, p. 125 I-3136-85:USGS USGSOCFL 

CX017 Lead, USGS digest, 

extract AAS 

Lead recoverable from unfiltered water by 

dilute HCl (USGS) digestion, chelationextraction with APDC and MIBK, and 

AAS 

TWRI 5-A1/1989, p. 245 I-3400-85:USGS USGSOCFL 

FL001 U (natural), wf, 

fluorometric 

Dissolved fluorometric USGSNWQL 

FL002 U (natural) 2SPE, 

wf,fluorimetry 

Fluorimetry, Uranium, FIL USGSNWQL 

FL003 U (natural), water, 

fluorometric 

U, Fluorometric, TOT USGSNWQL 

FL004 U (natural), wf, FL-DIR 

GR-W 

U-FIL, fluorometric method--direct (FLDIR.), ground water (GR-W)  USGSNWQL 

FL006 U (natural), wf, direct 

fluorom 

U-FIL, fluorometric method--direct 

(FLUOR-direct) 

 USGSNWQL 

FL007 Uranium, wf,extract 

fluorometric 

U-FIL, fluorometric method--extracted 

sample (FLUOR-ext) 

 USGSNWQL 

GF031 Elements, drinking water, 

GFAA 

EPA 200.9 determination of total 

recoverable trace elements in drinking 

water by GFAA 

 200.9:USEPA USGSNWQL 

GF032 Antimony, drinking 

wtaer, GFAA 

GFAA, Antimony, dissolved, EPA 200.9, 

Drinking Water 

 200.9:USEPA USGSNWQL 

GF040 Antimony, wu, GFAA ANTIMONY TOTAL (GFAA) 204.2:USEPA USGSOCFL 

GF042 Arsenic, wf, GFAA ARSENIC DISSOLVED (GFAA) 206.2:USEPA USGSOCFL 

GF044 Arsenic, wu, GFAA ARSENIC TOTAL GFAA 206.2:USEPA USGSOCFL 

GF046 Barium, wf, GF BARIUM DISSOLVED (GF) 208.2:USEPA USGSOCFL 

GF056 Lead, LL, wu, GFAA LEAD TOTAL LL (GFAA) 239.2:USEPA USGSOCFL 
Page A9 of 10 

GF060 Selenium, wf, GFAA 

(Ocala) 

SELENIUM DISSOLVED (GFAA) 270.2:USEPA USGSOCFL 

GF073 Arsenic and selenium, 

wu, GFAA 

Graphite furnace atomic absorption I-0000-91:USGS USGSNWQL 

GF078 Lead, LIS, GFAAS Lead in low ionic strength water by 

GFAAS 

TWRI 5-A1/1989, p. 249 I-1401-85:USGS USGSNWQL 

GF084 Zinc, LIS, GFAAS Zinc in low ionic strength water by GFAAS TWRI 5-A1/1989, p. 509 I-1901-85:USGS USGSNWQL 

GF085 Arsenic, wf, GFAAS Arsenic in Filtered Water by GFAAS OF 98-639 I-2063-98:USGS USGSNWQL 

GF086 Cadmium, wf, GF-AAS Cadmium in filtered water, by GF-AAS OF 93-125, p. 53 I-2138-89:USGS USGSNWQL 

GF089 Lead, wf, GF-AAS Lead in filtered water, by GF-AAS OF 93-125, p. 87 I-2403-89:USGS USGSNWQL 

GF091 Selenium, wf, GFAAS 

(NWQL) 

Selenium in Filtered Water by GFAAS OF 98-639 I-2668-98:USGS USGSNWQL 

GF096 Arsenic, wu, GFAAS Arsenic Recoverable from Unfiltered Water 

by GFAAS 

OF 98-639 I-4063-98:USGS USGSNWQL 

GF097 Cadmium, wu, GF-AAS Cadmium in unfiltered water, by GF-AAS OF 93-125, p. 53 I-4138-89:USGS USGSNWQL 

GF100 Lead, wu, GF-AAS Lead recoverable from unfiltered water, by 

GF-AAS 

OF 93-125, p. 87 I-4403-89:USGS USGSNWQL 

HY010 Arsenic, wf, auto hydride 

AAS 

Arsenic in filtered water by automated 

sulfuric acid potassium persulfate digestion 

or ultraviolet radiation, hydride generation, 

and tube furnace AAS 

TWRI 5-A1/1989, p. 77 I-2062-85:USGS USGSNWQL 

HY011 Selenium, wf, auto 

hydride AAS 

Selenium in filtered water by automated 

hydride generation and AAS 

TWRI 5-A1/1989, p. 403 I-2667-85:USGS USGSNWQL 

HY013 Arsenic, wu, digest & 

hydrideAAS 

Total arsenic in unfiltered water by 

automated sulfuric acid potassium 

persulfate digestion or ultraviolet radiation, 

hydride generation, and tube furnace AAS 

TWRI 5-A1/1989, p. 77 I-4062-85:USGS USGSNWQL 

PHOS1 Laser phosphorim, dir 

(Eberline) 

Laser-induced phosphorimetry, direct 

(analysis by Eberline Services) 

 USGSNWQL 

PHOS2 Laser phosphorimetry, 

dir (NWQL) 

Laser-induced phosphorimetry, direct 

(analysis by NWQL) 

 USGSNWQL 

PHOS3 Laser phosphorim, ext 

(Eberline) 

Laser-induced phosphorimetry, extracted 

(analysis by Eberline Services) 

 USGSNWQL 

PHOS4 

PHOS5 Uranium, wf, dir 

phosphorescence 

Uranium, Water, Filtered, by Direct Laser 

Induced Phosphorescence 

 USGSNWQL 

PHOS6 Uranium, wf, ext 

phosphorescence 

Uranium, Water, Filtered, by Extraction 

and Laser Induced Phosphorescence 

 USGSNWQL 

PL120 Metals, wu, ICP-OES or 

ICP-MS 

Metals Recoverable from Unfiltered Water 

ICP-OES or ICP-MS 

OF 98-165 I-4471-97:USGS USGSNWQL 

PLA06 Trace elements, wf, 

ICP/T (Ocala 

Ocala Lab analysis of dissolved trace 

elements by Inductively Coupled Plasma--

Atomic Emission Spectrometry (ICP/T) 

 200.7:USEPA USGSOCFL 

PLA07 Trace elements, 

wu,ICP/T (Ocala) 

Ocala Lab analysis of total trace elements 

by Inductively Coupled Plasma--Atomic 

Emission Spectrometry (ICP/T) 

 200.7:USEPA USGSOCFL 

PLA11 Metals, wf, ICP-AES Metals, filtered water, Inductively coupled 

plasma-atomic emmission spectrometry 

OF 93-125, p. 101 I-1472-87:USGS USGSNWQL 

PLA15 Metals, wu, ICP-AES Metals, unfiltered water, Inductively 

coupled plasma-atomic emission 

spectrometry 

OF 98-165 I-4471-97:USGS USGSNWQL 

PLM10 Collision/reaction cell 

ICPMS 

Elements in Natural-Water, Biota, 

Sediment, and Soil Samples Using 

Collision/Reaction Cell Inductively 

Coupled Plasma-Mass Spectrometry 

USGS TMR 5-B1 :USGS USGSNWQL 

PLM27 Elements, wf, ICP-MS 

w/o digest 

Trace elements in water and wastes by 

inductively coupled plasma - mass 

spectrometry without digestion 

EPA-600/R-94/111 200.8:USEPA USGSOKWC 

PLM40 Metals, water, ICP-MS Metals in Water by ICP-MS OF 92-634 I-2477-92:USGS USGSNWQL 
Page A10 of 10 

PLM43 Metals, wf, ICP-MS Metals, filtered water, Inductively coupled 

plasma-mass spectrometry 

OF 92-634 I-2477-92:USGS USGSNWQL 

PLM47 Metals, wu, ICP-MS Metals, unfiltered water, Inductively 

coupled plasma-mass spectrometry 

OF 98-165 I-4471-97:USGS USGSNWQL 

PLM48 Metals, wu, ICP-MS Metals, unfiltered water, Inductively 

coupled plasma-mass spectrometry 

OF 98-165 I-4471-97:USGS USGSNWQL 
Page B1 of 6 

Appendix B: 

Discussion of Spatial Distributions and Analytical 

Artifacts in Groundwater Analyses from NWIS 

Database

B1. Spatial Distribution of Groundwater Analyses 

from NWIS Database 

With reference to discussion in Section 3.3.3, we further analyze here the spatial distributions of 

measured aqueous concentrations of lead in groundwater analyses from the NWIS database 

(Figure 3.8(a) and Figure B.1(a)). The question is why the measurable lead concentrations are so 

sparsely distributed over many parts of the country. A possible reason is that less water quality 

analyses for hazardous inorganic constituents have been conducted in certain parts of the country 

or that analyses have been conducted using analytical methods with insufficient detection limits. 

For evaluation, we have plotted in Figure B1(b) the spatial distribution of all samples that have 

been declared “non-detects” for lead, with the color scheme indicating the detection limit of the 

analytical method used (in Log mg/L). In Figure B1(c), we furthermore plot the location of 

samples that have been declared in the NWIS database as “presence verified, but not quantified”. 

Figures B1(a) through B1(c) together indicate that both possible reasons stated above are valid. 

Clearly, there are many states and regions where almost no water analyses for lead have been 

conducted, or at least these analyses have not been reported in the NWIS database. There are 

furthermore several states and regions with many lead analyses in the NWIS database, but the 

majority of those with insufficient detection limits. A good example are the states of Iowa and 

Illinois, where numerous samples have detection limits of about 0.01 mg/L, two orders of 

magnitude above the expected equilibrium lead concentration of 0.0001 mg/L for a typical 

reducing groundwater where galena coexists with pyrite and goethite (Section 3.3.5). The “hot 

spots” of measurable lead concentrations in Figure B1(a) are clearly correlated to states or 

regions where large numbers of high-quality analyses (i.e., with low enough detection limits) 

have been conducted. In contrast, states or regions with no or only few detectable lead 

concentrations in Figure 3B1(a) correspond to locations with either no measurements or analyses 
Page B2 of 6 

with insufficient detection limits. We may conclude that almost all groundwaters in the United 

States would exhibit small (mostly below MCLs), but non-zero lead concentrations if measured 

with appropriate analytical techniques, which is consistent with the above finding that lead 

occurs ubiquitously in soils, sediments and aquifer rocks throughout the United States. While not 

reported here, the same conclusions can be drawn for the other hazardous inorganic constituents 

of interest in this study. 
Page B3 of 6 

(a) 

(b) 

(c) 

Fig. B1. Spatial distribution of (a) samples with measured aqueous concentration (Log mg/L) of lead, (b) 

samples with non-detects for lead with color scheme indicating the detection limit of the analytical 

method (in Log mg/L), and (c) samples with text stating “presence verified, but not quantified”. The 

plots were drawn from more than 38,000 groundwater quality analyses downloaded from the National 

Water Information System Database (NWIS). 
Page B4 of 6 

B2. Characterization of Analytical Artifacts in the 

Measurement of Concentrations of Hazardous 

Inorganic Constituents 

Section 3.3.5 discusses the possibility and the implications of analytical artifacts introduced by 

the differing accuracy and sensitivity of analytical methods. The Cd concentrations in Figure 

3.6(d) were mentioned there as an example where almost all analyses may have been affected. 

The figure shows essentially two sets of measurements, with each set starting an initially high 

frequency, apparently corresponding to the detection limit for some of the analyses, which then 

falls off with increasing concentration until a relatively low frequency is recorded. This is, of 

course, very different from a normal or possibly log-normal distribution of concentrations to be 

expected in natural groundwaters. 

The analogy of the observations in Figure 3.6(d) with radioactive decay suggests a similar 

process involving Poisson statistics. This is illustrated by Figures B2(a) and B2(b), which display 

the normalized analytical frequency versus log concentration for two selected analytical methods 

(i.e., PLA11 with a detection limit of 0.001 mg/L and up, and PLM43 with a detection limit of 

0.00002 mg/L and up, see Table A1). A linear regression of the plots indicates a very good 

correlation between frequency and concentration, with R2 = 0.98 for both analytical methods. 

The normalized frequency of “non-detects” for each method is plotted as the negative value of 

the detection limit, and is included in the regression analysis. 

The theoretical basis for these correlations has not been evaluated, but probably relates to 

contamination or unknown interferences in the analytical method, as replicate analyses for these 

methods do not show such large spurious variations. Clearly, further study of this phenomenon 

is warranted, especially in relation to the analytical sensitivity of the method and its suitability 

for detecting actual concentrations expected in a given ground water sample. 

Similar evaluations of selected analytical methods for other elements yield similar correlations. 

For example, Figure B3 is a plot of the normalized analytical frequency versus linear 

concentration for Hg analyses using the CV013 analytical method with a detection limit no lower 

than 0.0001 mg/L, suggesting the majority of reported Hg analyses are also artifacts. After 
Page B5 of 6 

discounting for potential artifacts, the remaining chemical analyses are of marginal utility in 

predicting thermodynamic controls for mercury concentrations in ground waters, and therefore a 

theoretical interpretation of mercury behavior may be the only recourse for predicting the 

response of dissolved mercury to elevated partial pressures of CO2. Figures B4(a) and B4(b) 

show similar artifacts are operative for lead when less sensitive analytical methods are utilized. 

(a) 

y = -0.3274x - 0.5116

R2

 = 0.982

-4

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0

-2 0 2 4 6 8 10

Cd Concentration (mg/L x 10^-3)

Log Normalized Frequency (-))

(b) 

y = -0.1813 x - 0.5 802

R2 = 0.9832

-2

-1.8

-1.6

-1.4

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

-3 -2 -1 0 1 2 3 4 5 6 7

Cd Concentration (mg/L x 10^5)

Log Norma lized Frequency (-)

Fig. B2. Log normalized frequency of chemical analyses as a function of cadmium concentration in ground 

waters at and above the detection limit for the analytical method. (a) PLA11; (b) PLM43. 
Page B6 of 6 

 

y = -0.2796x - 0.7661

R2

 = 0.9536

-4

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0

-4 -2 0 2 4 6 8 10 12

Hg Concentration (mg/L x 10^-4)

Normalized Log Frequency (-)

Fig. B3. Log normalized frequency of chemical analyses as a function of mercury concentration in ground 

waters at and above the detection limit for the CV013 analytical method. 

(a) 

y = -0.5094x - 0.5497

R2

 = 0.9909

-3

-2.5

-2

-1.5

-1

-0.5

0

-2 -1 0 1 2 3 4 5

Pb Concentration (mg/L x 10^2)

N orm alize d Lo g Freq uen cy (-)

(b) 

y = -0.2483x - 0.6746

R2

 = 0.9537

-3

-2.5

-2

-1.5

-1

-0.5

0

-2 0 2 4 6 8 10

Pb Con centration (mg/L x 10 ^3)

Normaliz ed Log Frequ enc y (-)

Fig. B4. Log normalized frequency of chemical analyses as a function of lead concentration in ground waters at 

and above the detection limit for the analytical method. (a) PLA11; (b) CX007. 
 C1 of 32 

Appendix C: 

Thermodynamic Database Augmentation 

C1. Introduction 

A substantial augmentation of solubility products of solid phases and dissociation constants of 

aqueous species is required to adequately model the complex chemistry associated with the 

behavior of hazardous inorganic constituents in ground waters. The augmentation is not limited 

merely to those primary minerals and secondary alteration products where the hazardous 

constituent is an essential component; attention must also be paid to minerals that interact with 

the hazardous constituents through co-precipitation, ion exchange, and/or adsorption. 

Furthermore, the database used for EQ3/6 simulations, Data0.dat.YMP, suffers from 

obsolescence. The solubility products of many common rock-forming minerals listed in the 

database require revision to more accurately reflect advances since the original compilations 

used in the database were published. 

Appendix C is subdivided into several sections dealing respectively with additional aqueous 

speciation and minerals for arsenic, lead, antimony and selenium. Corrections are also provided 

for Fe+2 and Fe+3 aqueous species, and an expanded database is provided for metastable iron 

oxides, oxy-hydroxides, and sulfides. Supplementary information for carbonates and chlorite is 

also included, because of their relevance in participating as pH or Eh buffers. The appendix 

concludes with a section in which data are presented on minerals that had to be included on a 

case-by-case basis in order to ensure that the geochemical model reflected reality. 

C2. Arsenic 

C2.1 Background 

The presence of arsenic in groundwater has long been a serious problem in many parts of the 

world, particularly in the Ganges delta of Bangladesh (Smedley, 2003), where farmers draw 

drinking water from shallow aquifers that are subject to seasonal oxidation and mobilization of 

arsenate. In the United States, several areas are also characterized by elevated arsenic 

concentrations in ground waters, notably in northwestern Nevada, where the municipal water of 
 C2 of 32 

the city of Fallon has unacceptably high levels (Smedley, 2003) and the Rio Grande Rift of New 

Mexico (Bexfield and Plummer, 2003). The city of Albuquerque is forced to treat its water 

supplies to mitigate excessive concentrations of As. Other regions of the country, such as the 

Upper Michigan Peninsula and parts of Minnesota (Thornberg and Sahai 2004) and New 

Hampshire (Utsunomiya et al., 2003), are also affected by high As levels in groundwater. 

Increasing use of aquifer storage and recovery techniques (ASR) has also lead to arsenic 

contamination of the recovered water (Arthur et al., 2005). A significant proportion of the 

sampled more than 38,000 well water samples taken from the NWIS database already possess As 

levels exceeding current MCLs. The introduction of high pressure CO2 into aquifers with such 

elevated ambient As concentrations could aggravate an already marginal water quality in some 

areas. Therefore, the evaluation of As chemistry in groundwaters is very important, and takes 

precedence over some other hazardous constituents in relation to the present problem. 

The chemistry of As in surficial waters and shallow subsurface groundwaters is extraordinarily 

complex. As occurs naturally in at least five oxidation states (-3, 0, +2, +3 and +5). Both +3 and 

+5 states form hydroxy-anions in aqueous solution with varying degrees of protonation. Both 

are noted for reactions with Fe in both +2 and +3 states. As(V) adsorption onto hydrated ferric 

oxides (HFO) stabilizes the latter in a metastable or colloidal or nanoparticulate form, which can 

persist for tens to hundreds of years. In the presence of Ca+2, Ca-As-Fe-OH macro complexes 

form in solution, which can aggregate to form glassy or amorphous minerals such as pitticite or 

yukonite. As an added complication, it is well known that the transformation of aqueous As(III) 

to As(V) and vice versa is sluggish (Cherry et al., 1979). Aqueous As concentrations in ground 

waters are therefore likely to be dictated not only by the formation of metastable phases and 

adsorption-stabilized colloids, but also by kinetically hindered redox transformations. 

A substantial literature has evolved in which the thermodynamic and kinetic properties of As 

species have been studied and elucidated, mainly because of the interest in groundwater 

treatment, and the need to remediate contamination from current and former mining operations. 

The literature has been searched and thermodynamic data compiled (or in some cases estimated) 

for 24 aqueous species and complexes, and 15 minerals or potential stabilizing precipitates. 

Most of these data have been formatted for entry in the EQ3 Data0.dat and TOUGHREACT 

thermodynamic databases in the form of relevant dissociation constants or solubility products at 
 C3 of 32 

25°C. Additional calculations were also conducted using SUPCRT92 (Johnson et al., 1992) to 

generate solubility and dissociation constant data wherever feasible. 

One aspect of As chemistry, which has not been adequately addressed in this report, concerns the 

substitution of As for S in pyrite. One of the principal causes of As contamination in aquifers 

occurs through the oxidation of arsenian pyrite. Reich and Becker (2006) have formulated a 

theoretical solid-solution model quantifying As substitution for S in both pyrite and marcasite. 

Time constraints prevented the incorporation of this model in the TOUGHREACT simulator. 

Instead, an empirical correction factor was applied to both arsenopyrite and gudmundite 

solubility products to account for their dilution from unit activity due to solid solution in pyrite. 

This was accomplished by reconciling field observations of both As and Sb concentrations in 

NWIS potable groundwater samples with thermodynamic predictions of their concentrations, as 

described in Section 3.4. 

In the following two sections, methods are described for building an expanded internally 

consistent database of thermodynamic properties of aqueous As species and solid As phases, 

respectively, based on an earlier study by Nordstrom and Archer (2003). 

C2.2 Aqueous Arsenic Species. 

C2.2.1 Calculation of HKF Equation of State Parameters for Aqueous 

Arsenate and Arsenite Species 

The thermodynamic properties of aqueous arsenic species as derived by Nordstrom and Archer 

(2003) were used as a point of departure. They are summarized in Table C1. Entropy, SPr ,Tr

0

, of 

two species, HAsO3

-2, and AsO3-3, needed to be estimated. This was accomplished through a 

correlation plot of the entropies of several oxyanion species as listed in Table C2, and illustrated 

in Figure C1. 
 C4 of 32 

Table C1. Thermodynamic properties of Arsenic Solid Phases and Aqueous Species (from Nordstrom and 

Archer, 2003) 

Phase 0

f Pr Tr ΔG , ,

cal mol-1 

ΔHf ,Pr ,Tr

0

cal mol-1

SPr ,Tr

0 

cal mol-1 K-1 

CPr ,Tr

0

cal mol-1 K-1

As 0 0 8.516 5.839 

S 0 0 7.600 5.402 

O2(g) 0 0 49.003 7.016 

H2(g) 0 0 31.208 6.889 

H2O(l) -56687 -68315 16.709 17.995 

As2O3(cub) -137749 -157091 25.664 23.155 

As2O3(mon) -137794 -156709 27.096 23.179 

As2O5 -185220 -219309 25.201 27.701 

S2(g) 18953 30681 54.510 7.761 

AsS(a) -7481 -7600 15.033 11.233 

AsS(b) -7385 -7409 15.177 11.233 

As2S3(a) -20292 -20507 39.149 38.958 

As2S3(am) -18356 -15989 47.801 

 

Phase 0

G f Pr Tr Δ , ,

cal mol-1

ΔG f ,Pr ,Tr

0

cal mol-1

SPr ,Tr

0

cal mol-1 K-1

CPr ,Tr

0

cal mol-1 K-1

H2S(aq) -6661 -9610 28.920 39.914 

HS-(aq) 2880 -4216 15.010 -21.989 

As3S4(SH)2

-

 -30019 

AsS(OH)(SH)- -58413 

H3AsO3(aq) -152971 -177428 46.804 

H2AsO3

-1 -140454 -170827 26.957 

HAsO3

-2 -121272 (-1.839) 

H3AsO4(aq) -183258 -215930 43.755 

H2AsO4- -180127 -217835 26.859 

HAsO4

-2 -170586 -217115 -2.729 

AsO4

-3 -154484 -212765 -42.139 

AsO3

-3 -100813 (-46.715) 

Table C2. Standard Partial Molal Entropies, SPr ,Tr

0

, of Selected Oxyanions

Charge SPr ,Tr

0 , cal mol-1 K-1 

 AsO4

-3 AsO3-3 PO4-3 PO3-3 SO4-2 CH3COO0 43.755 46.804 43.6 42.7 

1 26.859 26.957 21.6 31.7 30 20.6 

2 -2.729 -8 4 4.5 

3 -42.139 -53 
 C5 of 32 

y = -7.2936x2 - 8.4074x + 44.15

R2

 = 0.9801

-60

-40

-20

0

20

40

60

0 0.5 1 1.5 2 2.5 3 3.5

Charge

Fig. C1. Regression analysis of standard partial molal entropies, SPr ,Tr

0

, of selected oxyanions as a function of 

charge. 

A regression analysis through all data points yielded the equation listed in Figure C1, with a 

regression coefficient, R2

, = 0.9801. The calculated values of SPr ,Tr

0

at Z = -2 and -3 were used as 

estimates for HAsO3

-2 and AsO3-3. The heat capacities, CPr ,Tr

0

 of all species were then calculated 

using equations derived by Shock et al. (1997; Table 6), which are for hydrogen bearing anions 

CPr ,Tr

0

=1.65SPr ,Tr

0

− 46.8 

and for hydrogen-free anions where zj = -3 

CPr ,Tr

0

= 0.60SPr ,Tr

0

− 92.8 

Calculation of the HKF equation of state parameters, c1, c2 and the Born coefficient, ωPr ,Tr

, then 

follows with application of the following equations (Shock and Helgeson, 1988; Shock et al., 

1989). For charged species, the Born coefficient can be calculated from: 
 C6 of 32 

ω j,Pr ,Tr = ηZ j

ηZ j

re, j,Pr ,Tr

− 1

3.08

⎡

⎣ 

⎢ ⎢ 

⎤ 

⎦

⎥ ⎥ 

where η = N0

e

2

2 =1.66027 ×105 A

0

 cal mol-1, with N0 Avogadro’s number and e the absolute 

electronic charge with a value of 4.80298 ×10−10 esu. re, j,Pr ,Tr

is the effective electrostatic radius of 

the ion 

re, j,Pr ,Tr = Z j

2 ηYPr ,Tr ( −100)

SPr ,Tr

0

−α z

where YPr ,Tr

 has the value −5.802 ×10−5

K−1

 and refers to the Born function 

Y = 1

ε

∂ lnε

∂T

⎛

⎝ ⎜ ⎞⎠ ⎟ 

P

and α z is a charge dependent correlation parameter 

α z = 71.5 Z j

For neutral aqueous inorganic species, the effective Born coefficient is 

ωe = −1514.4SPr ,Tr

0

+ 0.34 ×105

Calculation of c1 proceeds through the following steps. The heat capacity of an ion is assumed to 

consist of two components, thus 

CP

0

= ΔCP,n

0

+ ΔCP,s

0

representing respectively the nonsolvation and solvation contributions. The Born coefficient can 

be used to calculate ΔCP,s

0

, thus 

ΔCPr ,Tr ,s

0

=ωTX
 C7 of 32 

where X is the standard partial molal heat capacity Born Function (Helgeson and Kirkham, 

1974) 

X = 1

ε

∂

2 lnε

∂T2

⎛ 

⎝ ⎜ 

⎞

⎠ ⎟ 

P

− ∂ lnε

∂T

⎛ 

⎝ ⎜ ⎞ ⎠ ⎟ 

P

2 ⎛

⎝ 

⎜ ⎜ 

⎞ 

⎠ 

⎟ ⎟ 

which at Pr,Tr has the value of 3.09K−2 ×10−7

. 

ΔCP,n

0

 can then be obtained by difference. Furthermore, we have 

ΔCP,n

0

= c1 + c2

1

T −θ

⎛ 

⎝ ⎜ ⎞ ⎠ ⎟ 

2

where c2 ×10−4 = 0.2037PPr ,Tr

0

− 3.0346 and θ = 228K. 

The general form of the HKF equation of state is given by 

( ) ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ − + ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ Δ = Δ − − − r

r

r

o

Tr

o

f

o T T

T

T G G S T T c T Tr Pr, 1 ln Pr,

 ( )

( ) ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

⎥

⎦

⎤ ⎢

⎣

⎡

−

− ⎟ −

⎠

⎞ ⎜

⎝

⎛ − ⎥

⎦

⎤ ⎢

⎣

⎡

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

− ⎟ −

⎠

⎞ ⎜

⎝

⎛

− − θ

θ

θ θ

θ

θ θ r

r

r T T

T T T T

T T

c ln 1 1

2 2

 +a1 P − Pr ( ) + a2 ln Ψ + P

Ψ + Pr

⎛

⎝ 

⎜ ⎞ 

⎠ 

⎟ + a3

P− Pr

T − θ

⎛ 

⎝ ⎞ ⎠ + a4

1

T − θ

⎛ 

⎝ ⎞ ⎠ ln Ψ + P

Ψ + Pr

⎛

⎝ 

⎜ ⎞

⎠

⎟ 

 ( ) [ ] ⎟

⎠

⎞ ⎜

⎝

⎛ ⎥ + − −

⎦

⎤ ⎢

⎣

⎡

+ − + − 1 1 1 1

Pr, Pr,

Pr

Pr,

ε

ω ω

ε ε

ω Tr r Tr

Tr

Tr Y T T k ; 

V o = a1 + a2

1

Ψ + P

⎛ 

⎝ ⎞ ⎠ + a3 + a4

1

Ψ + P

⎛ 

⎝ ⎞ ⎠ 

⎛

⎝ 

⎞ 

⎠ 

1

T − θ

⎛ 

⎝ ⎞ ⎠ − ωQ +

1

ε

−1 ⎛ 

⎝ ⎞ ⎠ 

∂ω

∂P

⎛

⎝ 

⎞ 

⎠ T

; 

( ) ( ) ( ) ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

Ψ +

Ψ + − + ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

− − − = +

r

r

o

P P

P

a P P a

T

T

T

c

C c ln 2

2 3 3 4

2

1

θ θ

 ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

⎟

⎠

⎞ ⎜

⎝

⎛ ⎟ − − ⎠⎞ ⎜

⎝

⎛ + + 2

2

1 1 2

T

T

T

TX TY

P ∂

∂ ω

∂ ε

∂ω

ω 
 C8 of 32 

where and [k ] is a switch function equal to 1 for charged species and 0 for neutral species 

(Sverjensky et al., 1997), and [Q], [Y ], and [ X ] are given by (Tanger and Helgeson, 1988) 

 Q ≡ − ∂( ) 1 ε

∂P

⎛

⎝ 

⎜ ⎞ 

⎠ T

= 1

ε

2

∂ε

∂P

⎛ 

⎝ 

⎞ 

⎠ T

; 

 Y ≡ − ∂( ) 1 ε

∂T

⎛

⎝ 

⎜ ⎞ 

⎠ P

= 1

ε

2

∂ε

∂T

⎛ 

⎝ 

⎞ 

⎠ P

; 

 X ≡ ∂Y

∂T

⎛ 

⎝ 

⎞ 

⎠ P

= 1

ε

2

∂2ε

∂T2

⎛ 

⎝ 

⎜ ⎞ 

⎠ P

− 2

ε

∂ε

∂T

⎛ 

⎝ 

⎞ 

⎠ P

2 ⎛

⎝ 

⎜ ⎞ 

⎠ 

⎟ 

and can be computed from equations expressing the dielectric properties of water to 1000°C and 

5 kbar (Johnson and Norton, 1992). 

However, the parameters a1,a2,a3 and a4 relate only to corrections for pressure. Because 

potable ground waters are obtained from relatively shallow wells, correction of ΔG f , j

0

 for 

pressure would be trivial and almost certainly within the uncertainties associated with the 

derivations under discussion. 
 C9 of 32 

C2.2.2 Calculation of HKF Equation of State Parameters for Metal 

Complexes with Aqueous Arsenate and Arsenite Species 

Langmuir et al. (2006) present estimated values of the association constants of a number of metal 

arsenate complexes. Those complexes relevant to the problem at hand are listed in Table C3. 

Table C3. Association Constant and Gibbs Free Energy of Formation of Selected Arsenate Complexes (after 

Langmuir et al., 2006) and Derived HKF Equation of State Parameters 

Complex logKassoc

at 25°C 

ΔG f ,Pr ,Tr

0

cal mol-1 

SPr ,Tr

0

cal mol-1

K-1

CPr ,Tr

0 cal 

mol-1 K-1

c1

cal mol-1

K-1

c2

cal K mol-1

x 10-4

ωPr ,Tr

cal mol-1

x 10-5

Z 

Arsenite Species 

H4AsO3

+

 -0.29 -116510 -81.203 +1 

H3AsO3(aq) -152971 46.80 30.43 20.6012 3.1635 -0.3688 0 

H2AsO3

-

 -9.17 -140454 26.96 -2.32 15.9835 -3.5072 1.2132 -1 

HAsO3

-2 -23.27 -121272 -1.84 -49.83 7.0916 -13.1859 3.2706 -2 

AsO3

-3 -38.27 -100813 -46.71 -120.83 -13.3179 -27.6474 5.5714 -3 

Arsenate Species 

H3AsO4(aq) -183258 43.75 25.40 18.0776 2.1384 -0.3226 0 

H2AsO4

-

 -2.30 -180127 26.86 -2.48 15.9024 -3.5402 1.2146 -1 

HAsO4

-2 -9.29 -170590 -2.73 -51.30 6.3549 -13.4851 3.2840 -2 

AsO4

-3 -21.09 -154489 -42.14 -118.08 -12.3472 -27.0882 5.5021 -3 

MgH2AsO4

+

 -0.78 -290706 25.86 42.18 32.2896 5.5571 0.1524 1 

MgHAsO4(aq) -6.43 -282997 -19.38 -6.64 8.1099 -4.3879 0.6336 0 

MgAsO4

-

 -14.75 -271645 -74.45 -73.42 -11.5410 -17.9910 2.7487 -1 

CaH2AsO4

+

 -1.24 -313693 45.36 41.23 29.0125 5.3636 -0.1429 1 

CaHAsO4(aq) -6.60 -306380 1.19 -7.59 4.6829 -4.5814 0.3220 0 

CaAsO4- -14.87 -295096 -52.80 -74.37 -15.1173 -18.1845 2.4209 -1 

AlH2AsO4

+2 0.77 -300826 -0.73 15.02 25.0924 0.0246 1.0989 2 

AlHAsO4

+

 -2.00 -297046 -48.43 -33.80 -1.8770 -9.9204 1.2773 1 

AlAsO4(aq) -6.99 -290238 -105.95 -100.58 -34.8667 -23.5235 1.9445 0 

MnHAsO4(aq) -5.54 -230206 -1.66 -5.40 6.3638 -4.1353 0.3651 0 

MnAsO4

-

 -14.96 -217353 -55.80 -72.18 -13.4157 -17.7384 2.4663 -1 

FeH2AsO4

+

 0.38 -205421 33.56 39.62 29.7151 5.0356 0.0358 1 

FeHAsO4(aq) -5.75 -197057 -11.26 -9.20 5.4762 -4.9094 0.5105 0 

FeAsO4

-

 -14.03 -185759 -65.90 -75.98 -14.2337 -18.5124 2.6193 -1 

FeH2AsO4

+2 1.74 -200131 10.05 28.92 31.7358 2.8560 0.9358 2 

FeHAsO4

+

 0.57 -198535 -37.03 -19.90 4.6789 -7.0890 1.1047 1 

FeAsO4(aq) -2.19 -194769 -93.93 -86.68 -28.3985 -20.6920 1.7624 0 

ZnHAsO4(aq) -6.08 -210169 -12.21 -6.63 7.1149 -4.3859 0.5249 0 

CdHAsO4(aq) -5.58 -194212 -2.93 -4.80 6.8921 -4.0131 0.3843 0 

PbH2AsO4

+

 -0.77 -187925 63.06 34.82 22.7867 4.0579 -0.4109 1 

PbHAsO4(aq) -6.25 -180447 19.86 19.86 19.8614 1.0112 0.0392 0 
 C10 of 32 

Using the information supplied by Nordstrom and Archer (2003), ΔG f , j

0

 can be calculated, as 

also listed in Table C3. A necessary requirement for calculating the HKF equation of state 

parameters is quantification of SPr ,Tr

0

 and CPr ,Tr

0

, neither of which have been measured or 

estimated. Calculation of SPr ,Tr

0

 is facilitated by the observation by Sverjensky et al. (1997) that 

the standard partial molal entropies of associationΔSassoc.,Pr ,Tr

0

of metal sulfate complexes 

MZ +2 + SO4

2− = MSO4

Z

 of a given charge, Z, are essentially identical, as indicated in Figure C2. 

Fig. C2. Standard partial molal entropies of associationΔSassoc.,Pr ,Tr

0

of metal sulfate complexes (after 

Sverjensky et al., 1997) 

These correlations were summarized by the authors in the form of the following equation: 

ΔSassoc.,Pr ,Tr ,y=1

0

=α Z ,SO4

2− S M Z +2

0

+ β Z,SO4

2−

where α Z,SO4

2− = −0.055Z + 0.055 andβ Z ,SO42− =13.84Z +18.16. 

Recognizing that the entropies of oxyanions of identical charge are nearly identical, as shown in 

Table C2 and Figure C1, above, the assumption was made that ΔSassoc.,Pr ,Tr

0

for metal arsenate 

complexes with charges equivalent to those of metal complexes would be equivalent. Thus, for 
 C11 of 32 

example, values of ΔSassoc.,Pr ,Tr

0

 for CaHAsO4(aq) are equivalent to CaSO4(aq). The estimated 

values of SPr ,Tr

0

 all metal complexes listed were calculated using the above equation, and are 

presented in Table C3. 

The estimation of CPr ,Tr

0

 for metal arsenate complexes could not be accomplished by reference to 

the existing papers describing parameter estimations for the HKF equation of state. The normal 

procedure is to use correlation plots relating CPr ,Tr

0

to SPr ,Tr

0

for given families of ionic or 

molecular species, e.g., Schock and Helgeson (1988, Eq. 91) and Shock et al. (1989, Eq. 24). 

However, in this case, no such correlation plots have been developed, and published plots are 

neither relevant nor adaptable for to arsenate complexes. Perusal of limited CPr ,Tr

0

data available 

for metal complexes of oxyanions presented in Sverjensky et al. (1997) indicates that the 

ΔCP,assoc.,Pr ,Tr

0

 for these species are all of similar magnitude, i.e., circa 50 cal mol-1 K, suggesting 

the formation of outer sphere complexes, as proposed many years ago by Garrels and Christ 

(1965, Figure 4.4, p. 100). Table C4 presents the results of this analysis. Among those listed 

complexes is MnSO4(aq). However, Sverjensky et al. (1997), referring to supporting literature, 

have asserted that the complexation of Fe+2 Mn+2 and Zn+2 involves the release of one or more 

molecules of water from the inner coordination sphere of central cations in such complexes, and 

indeed, this may be consistent with the elevated ΔCP,assoc.,Pr ,Tr

0

 values for CH3COO- complexes of 

Fe+2 and Zn+2. Compensating for one mol of water would result in a correction of –18.0 cal mol1

 K-1 to ΔCassoc.,Pr ,Tr

0

 for CH3COOFe+ and CH3COOZn+ leading to equivalent values of 65 and 

67.33 cal mol-1 K-1, respectively, which is more in accord with ΔCassoc.,Pr ,Tr

0

 for the oxyanion 

complexes listed in Table C4. Note, however, that ΔCassoc.,Pr ,Tr

0

 for the reaction between 

CH3COOH(aq) and either Fe+2 or Zn+2 is also approximately 50 cal mol-1 K-1. Bearing in mind 

the tentative nature of the analysis, we have made a provisional assumption that CPr ,Tr

0

 for all 

metal arsenate complexes listed in Table C3 can be estimated assuming that ΔCassoc.,Pr ,Tr

0

 = 50 cal 

mol-1 K-1 for all association reactions. 
 C12 of 32 

Table C4. A Comparison of Heat Capacities of Dissociation of selected Oxyanions 

Reaction Participating Species CPr ,Tr

0

ΔCassoc.,Pr ,Tr

0

K+ -3.0 

SO4

-2 K -64.38 +

 + SO4

-2 =KSO4-

KSO4

-

 -10.9 

56.48 

K+ -3.0 

HSO4

- K 5.3 +

 + HSO4

-

 =KHSO4(aq) 

KHSO4(aq) 57.72 

49.42 

Mn+2 -4.1 

SO4

-2 Mn -64.38 +2 +SO4

-2 = MnSO4(aq) 

MnSO4(aq) -20.6 

47.88 

Ca+2 -7.53 

CO3

-2 Ca -69.5 +2 +CO3

-2 = CaCO3(aq) 

CaCO3(aq) -29.6 

47.43 

Na+ 9.06 

HSiO3

- Na -21 +

 + HSiO3

-

 = NaHSiO3(aq) 

NaHSiO3(aq) 24.61 

36.55 

Ca+2 -7.63 

HSiO3

- Ca -21 +2 + HSiO3

-

 = CaHSiO3

+ 

CaHSiO3

+

 32.87 

61.5 

Average Value 49.9 

Fe+2 -7.9 

CH3COO- Fe 6.2 +2 + CH3COO-

 = CH3COOFe+

CH3COOFe+

 81.3 

83.0 

Zn+2 -5.33 

CH3COO- Zn 6.2 +2 + CH3COO-

 = CH3COOZn+

CH3COOZn+

 86.2 

85.33 

Fe+2 -7.9 

CH3COOH 40.56 Fe+2 + CH3COOH = CH3COOFe+

+ H+

CH3COOFe+

 81.3 

48.64 

Zn+2 -5.33 

CH3COOH 40.56 Zn+2 + CH3COOH = CH3COOZn+

+ H+ 

CH3COOFe+ 86.2 

51.07 

Calculation of the HKF equation of state parameters, c1, c2 and the Born coefficient, ωPr ,Tr

, 

follows the procedures outlined above. The respective values are listed in Table C3. 

C2.2.3 Derivation of Thermodynamic Properties of Arsenic Solid Phases 

and Minerals 

The following two tables summarize information on arsenic solid phases. Table C5 gives the 

crystallographic information necessary to calculate molar volumes of the solid phases, which are 

used in thermodynamic calculations to correct for the effects of pressure, and Table C6 provides 
 C13 of 32 

thermodynamic data for the solid phases. This information, in conjunction with the 

corresponding aqueous basis species information, is used to calculate the solid phase solubility 

products. 

The thermodynamic properties of some of the phases or minerals listed in Table C6 are 

incomplete. Information available in the literature may be sufficient to estimate preliminary 

values of ΔGPr ,Tr

0 for the minerals in question, but time constraints prevented further evaluation. 

In some cases, ΔGPr ,Tr

0 values were taken directly, or calculated from solubility product data 

reported in the literature, rather than by re-evaluating raw data and using revised thermodynamic 

data for aqueous species published by Nordstrom and Archer (2003) to recalculate solubility 

product and ΔGPr ,Tr

0 values. The tabulated data are not entirely consistent internally, and are 

therefore preliminary. However, the uncertainties introduced by taking this expedient approach 

are well within the uncertainties of the geochemical model developed in Section 3 of the report, 

and have no impact on the resulting findings and conclusions. 

A very recent paper by Perfetti et al. (2008) gives revised thermodynamic data for arsenopyrite. 

In this paper, ΔGPr ,Tr

0 for arsenopyrite is given as –32,612 cal/mol, which differs by +1,231 

cal/mol from the value reported in Table C6, which was cited from an earlier paper by Pokrovski 

et al. (2002). Using this revised value for the calculation of solubility products would decrease 

the extent of undersaturation of arsenopyrite reported in Section 3.4.5.2, Figure 3.34a, by 

approximately one unit in SI. This change would necessitate a smaller calibration correction 

(-1.8 vs. –3.0) to match the modeled As concentration in groundwaters in equilibrium with an 

arsenopyrite solid solution in pyrite, to that actually observed, as presented in Section 3.5.2, 

Table 3.13. This revised correction appears to be more realistic, although further corroborating 

evidence is needed. 
 

Table C5. Crystallographic Properties of Arsenic Solid Phases 

Cell Constants Name Formula 

ao (Å) bo (Å) co (Å) α β γ Z Space Group 

Arsenolite As2O3 11.07343 11.07343 11.07343 90 90 90 16 *Fd3m Claudetite II As2O3 7.99 4.645 9.115 90 78.3 90 4 P2_1/n Arsenic As 3.7597 3.7597 10.4412 90 90 120 6 R3m Arsenic Oxide As2O5 8.64 8.45 4.62 90 90 90 4 P212121 Ferrarisite Ca5(AsO3OH)2(AsO4)2.9H2O 8.294 6.722 11.198 106.16 92.94 99.2 1 P-1 Guerinite Ca5(AsO3OH)2(AsO4)2.9H2O 17.63 6.734 23.47 90 90.6 90 5 P2_1/n Haidingerite Ca(AsO3OH).H2O 6.904 16.161 7.935 90 90 90 8 Pcnb Hornesite Mg3(AsO4)2.8H2O 10.2573 13.4211 4.7526 90 105.076 90 2 C2/m Johnbaumite Ca5(AsO4)3(OH) 9.70 - 6.93 2 P 63/m or P63 Johnbaumite-Pb Pb5(AsO4)3(OH) Mimetite Pb5(AsO4)3(Cl) 10.211 - 7.4185 2 P6_3/m Pyromorphite Pb5(PO4)3(Cl) 9.9764 - 7.3511 2 P6_3/m Orpiment As2S3 11.475 9.577 4.256 90 90.68 90 4 P2_1/n Pharmacolite CaHAsO4.2H2O 5.9745 15.434 6.2797 90 114.83 90 4 Ia Pharmacosiderite KFe4(AsO4)3(OH)4.6H2O 7.9816 7.9816 7.9816 90 90 90 2 P-43m Picropharmacolite Ca4Mg(HAsO4)2(AsO4)2.11H2O 13.547 13.5 6.71 99.85 96.41 91.6 2 P-1 Pitticite Fe2(AsO4)(SO4)(OH).6H2O Realgar AsS 9.327 13.563 6.59 90 106.46 90 16 P2_1/n Pararealgar AsS 9.909 9.655 9.502 90 97.29 90 16 P2_1/c Scorodite FeAsO4.2H2O 8.942 10.075 10.339 90 90 90 Pbca Scorodite FeAsO4.2H2O 10.325 8.953 10.038 90 90 90 Pbca Symplesite Fe3(AsO4)2.8H2O 7.97 9.41 4.72 99.917 97.383 105.967 1 P-1 Talmessite Ca2Mg(AsO4)2.2H2O 5.874 6.943 5.537 97.3 108.7 108.1 1 P-1 Tooeleite Fe6(AsO3)4(SO4)(OH)4.4H2O 8.9575 6.4238 9.7912 90 96.032 90 1 C2/m Weilite CaHAsO4 7.0591 6.8906 7.2006 97.43 103.55 87.75 4 P-1 Yukonite Ca7Fe11(AsO4)9O10.24H2O Arsenopyrite FeAsS 5.744 5.675 5.785 90 112.28 90 4 P-1 
 

Table C6. Thermodynamic Properties of Arsenic Solid Phases 

CPr 0 cal mo

Mineral name Formula 

ΔGf ,Pr ,Tr 0

cal mol-1 

Ref ΔHf ,Pr ,Tr 0

cal mol-1

Ref SPr ,Tr 0

cal mol-1 K-1 

Ref VPr ,Tr 0

cm3 mol-1 

Ref 

a b ×103 

(C3RH2.25) Ca3(AsO4)2.2.25H2O -862066 88.361 3.2400 (C3RH3) Ca3(AsO4)2.3H2O -904227 96.911 3.2400 (C3RH3.67) Ca3(AsO4)2.3.67H2O -942878 2 104.549 3.2400 (C3RH4.25) Ca3(AsO4)2.4.25H2O -976338 2 111.161 3.2400 (C4RH5) Ca4(OH)2(AsO4)2.4H2O -1176881 2 119.981 4.3200 (F2R1.5SH) Fe4(AsO4)3(SO4)(OH) 106.309 31.0062 (FR1.5HX) Fe2(HAsO4)3.xH2O (FRX) Fe2(AsO4)x(HAsO4)y (R) As2O5 -185220 1 -219309 1 25.201 1 50.781 7 27.701 0.000 Arsenic As 0 1 0 1 8.516 1 13.049 8 5.137 2.602 Arsenopyrite FeAsS -33843 11 16.370 11 26.420 8 18.047 4.780 Arseniosiderite Ca2Fe3(AsO4)3O2.3H2O -887437 111.33 6 195.618 8 134.327 30.060 Arsenolite As2O3 -137749 1 -157091 1 25.664 1 50.724 8 8.37 48.6 Ca-arsenate Ca3(AsO4)2.xH2O -887437 49.761 100.070 8 61.700 13.900 Claudetite As2O3 -137794 1 -156709 1 27.096 1 47.257 8 23.197 9.326 Ferrarisite Ca5(AsO3OH)2(AsO4)2.9H2O -1866157 2 189.017 6 354.655 8 223.462 5.400 Guerinite Ca5(AsO3OH)2(AsO4)2.9H2O -1864962 2 184.458 6 335.580 8 223.462 5.400 Haidingerite Ca(AsO3OH).H2O -366396 2 35.591 6 66.991 8 40.476 1.080 Hornesite Mg3(AsO4)2.8H2O -887437 114.241 6 190.194 8 149.441 5.220 Johnbaumite Ca5(AsO4)3(OH) -1216375 2 94.817 6 170.031 103.457 5.400 Johnbaumite-Pb Pb5(AsO4)3(OH) -635516 3 123.721 6 204.215 90.357 32.000 Mimetite Pb5(AsO4)3(Cl) -630019 10 -713432 10 143.164 10 204.215 8 119.981 4.3200 Orpiment As2S3 -20292 1 -20507 1 39.149 1 70.503 8 22.73 13.82 As2S3,am As2S3 -18356 1 -15989 1 47.801 1 70.503 22.73 13.82 Pharmacolite CaHAsO4.2H2O 43.177 6 79.363 8 51.876 1.080 Pharmacosiderite KFe4(AsO4)3(OH)4.6H2O 162.563 6 306.084 8 184.967 42.605 Picropharmacolite Ca4Mg(HAsO4)2(AsO4)2.11H2O 199.568 6 361.370 8 244.772 10.385 Pitticite Fe2(AsO4)(SO4)(OH).6H2O 103.29 6 100.070 123.518 12.406 Realgar AsS -7481 1 -7600 1 15.033 1 29.743 8 12.94 -0.0133 Pararealgar AsS -7385 1 -7409 1 15.177 1 30.368 8 12.94 -0.0133 Scorodite FeAsO4.2H2O -306991 49.761 69.117 8 48.396 9.300 Scorodite,am FeAsO4.2H2O -303130 38.313 6 69.117 48.396 9.300 Symplesite Fe3(AsO4)2.8H2O -872736 119.583 6 198.385 8 155.261 6.210 Talmessite Ca2Mg(AsO4)2.2H2O 70.333 6 120.084 8 84.021 3.900 Tooeleite Fe6(AsO3)4(SO4)(OH)4.4H2O 177.878 6 337.409 8 161.252 66.906 Weillite CaHAsO4 27.344 6 51.858 8 29.076 1.080 Yukonite Ca7Fe11(AsO4)9O10.24H2O -940771 515.389 6 100.070 609.140 109.860 

Notes: 1. Nordstrom and Archer (2003); 2. Bothe and Brown (1999); Lee and Nriagu (2007); 4. Kelley (1960); 5. Calculated using the summation of oxide components. This work; 6. Calcula

Holland (1989). This work; 7. Jansen (1977); 8. Calculated from cell parameter data. 9. Pokrovski et al. (1996); 10. Flis et al. (2007), 0Pr Tr S , recalculated using elemental data from N
 C16 of 32 

C3. Lead 

C3.1 Introduction 

Incorporation of a wider range of lead solid and aqueous species in the ongoing evaluation of 

groundwater analyses, and in model development, is important for two reasons. First, the ingress 

of oxygen into aquifers that were formerly reducing, and where lead was presumably 

immobilized as the sulfide, galena, could induce solubilization of lead exceeding MCLs, even 

more so than would be induced through pH lowering through the ingress of high-pressure CO2. 

It is important to know whether the precipitation of secondary lead salts might restrain lead 

solubility under those conditions, especially with respect to those ground waters downloaded 

from the NWIS database, some of which exhibit lead concentrations that are supersaturated with 

respect to galena. Secondly, the addition of relevant aqueous lead complexes, especially those 

of carbonate, sulfide and selenide, will aid in a more precise calculations of lead concentrations 

in solutions saturated with respect to minerals containing lead as an essential component. These 

additional species might also affect the predicted sorption and ion exchange of lead on aquifer 

minerals, depending on the nature of the problem at hand. In this appendix, we present 

thermodynamic properties of lead minerals and other phases where lead is an essential 

component, and for all aqueous species except for lead sulfide and selenide complexes, which 

are discussed in Appendices D and E of this report. 

C3.2 Aqueous Lead Species 

An earlier unpublished compilation of internally consistent thermodynamic properties of a 

variety of lead carbonates, chlorides and sulfates, and a corresponding compilation relating to 

lead complexes in solution, was used to calculate their respective solubility products and 

dissociation constants in the Data0.dat database of EQ3 and the corresponding TOUGHREACT 

database. All respective constants were calculated using SUPCRT92, in which estimated HKFequation-of-state parameters for the aqueous species were included. In addition to replacing 

seven extant aqueous species and eleven solid phases in the database, three additional aqueous 

species, and thirteen additional solid phases were incorporated. Table C7 lists the 

thermodynamic properties of aqueous lead species together with their calculated HKF-equationof-state parameters. 
 C17 of 32 

C3.3 Lead Solid Phases and Minerals 

The following two tables summarize information on lead solid phases. Table C8 gives the 

crystallographic information necessary to calculate molar volumes of minerals, which are used in 

thermodynamic calculations to correct for the effects of pressure, and Table C9 provides 

thermodynamic data for the solid phase. This information, in conjunction with the corresponding 

aqueous basis species information is used to calculate the solid phase solubility products. The 

listed solid phases also include those known to very insoluble. An example is the Pb phosphate, 

pyromorphite, Pb5(PO4)3(Cl), in the presence of which lead concentrations are known to be 

depressed to low levels. The application of phosphate compounds as a means of controlling Pb 

mobility in contaminated soils has been suggested. The corresponding lead arsenate, mimetite, 

Pb5(AsO4)3(Cl), is also known to be very insoluble, and it is possible that mimetite could limit 

the saturation concentration of both As and Pb in ground waters. Therefore this mineral is also 

included as well as the arsenic analogue of apatite, johnbaumite, Pb5(PO4)3(OH). 

It should be noted that many of the data compiled in Tables C7, C8, and C9 were evaluated and 

reconciled over a decade ago. Time did permit only limited retrieval of new information 

published in the archival literature since that time. A casual perusal of the literature indicates 

that no substantive changes have occurred since the original work was compiled; rather, the data 

has been refined and augmented. Therefore, the application of more recent data, where 

available, would not change the findings of this report in any significant manner, and would be 

within the limits of geochemical model uncertainty. The advantage in using the current database 

is that it is internally consistent, and that the solubility product and dissociation constant data 

have been uniformly treated, as all calculations were performed using SUPCRT (Johnston et al., 

1992). However, a complete review of the data in the light of more recent published data is 

definitely recommended. 

C3.4 Acknowledgements 

We would like to acknowledge the contributions of Mr. Motomi Oshika, a guest scientist at the 

Berkeley Lab during 1992-3, who assisted in the evaluation of the thermodynamic properties of 

lead minerals, under a FY 1992 allocation of Exploratory R&D Funds to one of the authors (John 

Apps) for the project “Synthesizing Optical PbSO4 for Advanced Scintillation Applications”. 
 

Table C7. Thermodynamic Properties of Lead Aqueous Species 

Aqueous Species 

ΔGf ,Pr ,Tr 0 ΔHf ,Pr ,Tr 0 S o C po bf V oCg a1 uaa x10 a2 uaa x10−2 a3v.aa a4w.aa10−4 c1baa c2waa × 10−4 w

H+ 0 0 0 0 0 0 0 0 0 0 0 HCO3- -140282l -164898m 23.53m -8.46 24.6 7.5621 1.1505 1.2346 -2.8266 12.93965 -4.7579 CO3-2 - 126191m -161385m -11.95m -69.5n -5.02 2.8524 -.39844 6.4142 -2.6143 -3.3206 -17.1917 Pb+2 -5710h 220e 4.2e -12.74 -15.6 -0.0051 -7.7939 8.8134 -2.4568 8.6624 -5.6216 Pb(OH)+ -52089 -57652* 21.34 4.54 7.029 2.804 -0.9348 6.1174 -2.7404 10.9315 -2.1098 Pb(OH)2(aq) -95060 -111214* 41.57 33 48.127 8.4485 12.8476 .07206 -3.3101 22.8390 3.6875 Pb(OH)3- -137641 -166209* 40.06 15.1792 40.06 7.5921 10.7680 1.4938 -3.2241 24.4800 0.0574 PbCO3(aq) -141819 -156632* 40.72 29 46.880 8.2735 12.3922 0.9392 -3.2913 20.6134 2.8727 Pb(CO3)2-2 -272746 -301163* 70.01 -14.492 83.41 13.906 26.173 -4.5397 -3.8610 17.5025 -5.9866 PbCl+ -39046 -43260.* 28.0 3.400* 2.2870 -2.9703 8.5760 -2.6502 21.8 1.8542 PbCl2 -71190 -78281.* 45.0 22.400* 4.7542 3.9456 3.9447 -2.9421 22.2616 3.2801 PbCl3- -102147 -115066* 52.1 41.400* 7.7573 12.3640 -1.6927 -3.2901 39.698 5.521 PbCl4-2 -133254 -153342* 54.7 60.399* 10.7757 20.8252 -7.3588 -3.6399 57.0825 7.5985 Cl- -31379h -39933e 13.56 -29.44d 17.79d 4.0320 4.8010 5.5630 -2.847 -4.40 -5.714 OH- -3.7595h -54977e -2.56e -32.79d -4.18d 1.2527 0.0738 1.8423 -2.7821 4.15 -10.346 SO4-2 -177930h -217400e 4.50e -64.38d 13.88d 8.3014 -1.9846 -6.2122 -2.6970 1.64 -17.998 HSO4- -180630r -212500r 30.0r 5.3 35.2 6.9788 9.2590 2.1108 -3.1618 20.0961 -1.9550 Notes a: cal mol-1; b: cal mol-1K-1; c:cm3mol-1; d: Tanger and Helgeson; e: CODATA(1978); *: supcrt; f: from electrolytes in Table 10 unless otherwise indicated; h: calculat

and SO shown using standard entropiues of the elements from CODATA (1978); g: From electrolytes in Table 9 unless otherwise indicated; l: Helgeson, Kirkham, a

m: Berg and Vanderzee (1978); r: consistent with log KrPT, Δ HrPt AND Δ Srpt from dissociation reaction; u: Cal Mol-1xbar-1; v: cal K mol-1xbar-1; w: cal K mol-1; 

and 3; aa: calculated in a manner consistent with table 8 unless otherwise indicated 
 

Table C8. Crystallographic Properties of Lead Solid Phases 

 Notes a. Palache et al. (1951); b. Did not calculate; c. Cesbron and Schubnel (1968); d. Boivin et al. (1968), Tridot et al. (1969); e. Kuzel (197

Unit Cell Parameters Z Space Group 

Mineral Name Formula 

 ao bo co α β γ 4 

Cotunnite PbCl2 Kx 7.67 9.15 4.5 90 90 90 Pnam Palache e Å 7.68 9.17 4.51 4 Laurionite PbOHCl Å 9.72 4.06 7.11 90 90 90 Pcmn Palache e Kx 9.7 4.05 7.1  3PbO.PbCl2.nH2O Å 9.57 - - 90 90 90 Lorettoite Pb7O6Cl2 4 Palache eMendipite Pb3O2Cl2 Kx 9.50 11.87 5.87 90 90 90 Pnma Palache e Å 9.52 11.89 5.88 4 P212121Paralaurionite Pb(OH)Cl Kx 10.77 3.97 7.18 90 117.22 13/60 C21/m Palache e Å 10.79 3.98 7.19 Penfieldite Pb2OHCl3 Å 11.28 x 48.65 4 P6/m Cesbron aFieldlerite Pb3(OH)2Cl4 Kx 16.59 8.00 7.19 90 102.20 90 P21/a Palache e Å 16.62 8.02 7.20 4 Anglesite PbSO4 Kx 8.45 5.38 6.93 90 90 90 Pnma Palache e Å 8.47 5.39 6.94 4 Lanarkite Pb2(SO4)O Kx 13.73 5.68 7.07 90 116.22 90 C2/m  Å 13.76 5.69 7.08 2 Sulfate Dibasque 2PbO.PbSO4 Å 7.814 5.803 8.035 90 102.64 90 2 P21 or P21/m Boivin et (Second Source) Å 8.06 5.79 7.17 103  3PbO.PbSO4 Å 10.30 6.37 7.45 87.2 75.0 79.2 4 P1 or p1 Lanarkite PbO.PbSO4 Å 13.751 5.6969 7.0699 − 115.82 - 2 P  3PbO.PbSO4.H2O Å 10.30 6.37 7.45 87.2 75.0 79.2 4 P1 or p1 Boivin et 2nd Source 4PbO.PbSO4 Å 11.5234 11.7138 7.3152 90 91.04 90 P21/a Boivin et  11.443 11.666 7.316 90 90.82 90 Hydrocerussite Plumbonacrite Matlockite Lead Litharge Massicot 
 

Table C9. Thermodynamic Properties of Lead Solid Phases 

CPr 0 Coe

Mineral Name Formula G.F.W. 

g. .mol-1 ΔGf ,Pr ,Tr 0 cal.mol-1 Ref. ΔHf ,Pr ,Tr 0 kcal.mol-1 Ref. SPr ,Tr 0 cal.mol-1.k-1 Ref . VPr ,Tr 0 cm3.mol1 Ref. a 

Lead Pb 207.2 0.0 0.0 15.484 18.153 5.290 7.77 Litharge PbO 223.199 -45244 -52440 16.214 23.983 10.385 10.794 Massicot PbO 223.199 -44910 8,9,11 -51940 16.42 23.983 23 9.05 Lead Hydroxide Pb(OH)2 241.215 -108100 8,9,11,12 -123300 8,9,11 26.69 26 41.2 26 17.71 Cotunnite PbCl2 278.106 -75050 -85800 20 32.50 47.84 15.96 Anglesite PbSO4 303.258 -194353 -219870 35.51 47.95 10.96 Cerussite PbCO3 267.209 -150370 -168000 31.3 40.747 12.39 - (PbO)2.Pb(OH)2 687.613 58.9 26 90.33 15 38.91 Matlockite PbO.PbCl2 501.305 -126557 26 -144700 8,9 47.93 26 68.89 26.56 Lorretoite Pb7O6Cl2Mendipite (PbO)2.PbCl2 724.505 -176860 6 -199800 8,9 65.79 100.305 37.16 - (PbO)3.PbCl2 947.704 -253400 8,9 47.76 Lanarkite PbO.PbSO4 526.457 -246700 8,9,12 -276658 53.374 75.1 21.778 - (PbO)2.PbSO4 749.656 -294000 9 -328189 70.326 107.1 26 34.830 - PbO.(PbCO3)2 757.618 -351600 -395342 26 74.097 79.469 35.165 Laurionite PbClOH 259.660 -93470 9 -109402 26 28.83 42.26 16.835 Paralaurionite PbOHCl 259.660 -92340 -108361 26 28.53 26 41.36 26 16.835 Penfieldite Pb2OHCl3 537.766 61.22 89.71 32.795 Fieldlerite Pb3(OH)2Cl4 797.427 93.22 26 141.285 26 49.63 - (Pb(OH)2)3.PbCl2 1001.770 69.09 Hydrocerussite Pb(OH)2.(PbCO3)2 775.633 -408328 -456000 21 86.083 112.894 45.735 - (PbO)3.PbSO4 990.871 -341200 8 -388800 8,9 81.4 8,9 139.7 26 42.74 Plumbonacrite Pb10O(OH)6(CO3)6 2550.098 -1270767 -1448776 26 272.629 357.584 144.590 

Notes 1. Solans-Huguet and Font-Altaba (1967); 2. Derriche and Perrot (1976); 3. Kellogg (1989). 4. Esdaile (1967). 5. Kellogg and Basu (1960) Abdul-Samad e

(1980). 7. Loza et. al. (1984). 8. Wagman et al. (1968); 9. Wagman et. al. (1982); 10. Chase et. al. (1985); 11. Weast et al. (1984); 12. Dekock (1986). 14. Kel(1958). 16. Brasseur (1940); 17. Toptygina et. al. (1984). 18. Kuzel (1973) 19. Cesbron and Schubnel (1968). 20. Shelton (1970). 21. Ball and Casson (1977

23. Hill (1985). 24. Howie and Moser (1968), density referred to Clark and Tyler (1939). 25. SUPCRT. 26. This work.
 C21 of 32 

C4. Antimony and Selenium 

C4.1 Aqueous Antimony and Selenium Species 

Table C10 gives the thermodynamic properties for aqueous species of Sb(III). Sb(V) species, 

SbO2

+

 and Sb(OH)6

-

, were also included in the geochemical model using extant data in the EQ3 

Data0.dat database. The relevant selenite and selenate complexes included in the model were 

also extant data in the EQ3 Data0.dat database. The properties of selenide species used or 

derived in this study are discussed in Appendix E. 

C4.2 Antimony and Selenium Solid Phases and Minerals. 

Trace concentrations of selenides of heavy metals can play an important role in defining the 

solubility of hazardous constituents selected for study in this report. In Table C11, the 

crystallographic properties of selected selenides and antimony minerals are summarized with the 

purpose of calculating their molar volumes, which are given, together with other thermodynamic 

properties in Table C12. 
 

Table C10. Thermodynamic Properties of Antimony Aqueous Species 

Aqueous Species 

ΔG f ,Pr ,Tr 0

cal mol-1

SPr ,Tr

0

cal mol-1

K-1

CPr ,Tr

0 cal 

mol-1 K-1

V Pr ,Tr 0

cm3 mol-1

a1d x 10 

cal mol-1

bar-1

a2a x 10-2

cal mol-1

a3e

cal K mol-1

bar-1

a4f x 10-4

cal K mol1

c1

cal mol-1

K-1

c2 x 10-4

cal K mol-1 ωPr ,Tr

cal mol

Sb(OH) 3(aq) -154010 49.43 39.09 54 9.1415 14.5397 0.0630 -3.3801 28.6838 4.9284 -0.03Sb(OH) 4-1 -195038 42.28 22.96 44.58 8.1966 12.2325 0.9671 -3.2847 28.6515 1.6426 0.98H2Sb2S4(aq) -35488 69.36 71.3 70.7 11.4268 20.1196 -2.1236 -3.6107 47.5025 11.4892 -0.03HSb2S4-1 -28583 50.70 32.56 50.7 8.9913 14.1728 0.2068 -3.3649 33.0864 3.5984 0.854Sb2S4-2 -15622 42.38 -30.48 44.66 8.7546 13.5948 0.4332 -3.3410 12.3553 -9.2424 2.60

Note: Data after Akinfiniev et al. (1994) and Bessinger (2000) 

Table C11. Crystallographic Properties of Antimony and Selenium Minerals 

Name Formula Cell Constants 

ao (Å) bo (Å) co (Å) α β γ Z 

Space Group 

Naumannite Ag2Se 4.333 7.062 7.764 90 90 90 4 *P2_12_12_1 Berzelianite Cu2Se 5.765 5.765 5.765 90 90 90 4 Fm3m Antimonselite Sb2Se3 11.794 3.986 11.648 90 90 90 4 Pnma Clausthalite PbSe 6.1243 6.1243 6.1243 90 90 90 4 Fm3m Tiemannite HgSe 6.084 6.084 6.084 90 90 90 4 F-43m Ferroselite FeSe2 4.804 5.784 3.586 90 90 90 2 Pnnm Stibnite Sb2S3 11.3107 3.8363 11.2285 90 90 90 4 Pnma Antimony Sb 4.50661 4.50661 4.50661 57.108 57.108 57.108 20 R-3m Senarmonite Sb2O3 11.1519 11.1519 11.1519 90 90 90 16 Fd-3m Valentinite Sb2O3 4.911 12.464 5.412 90 90 90 4 Pccn Berthierite FeSb2S4 11.401 14.148 3.758 90 90 90 4 Pnam Gudmundite FeSbS 6.02 5.93 6.02 90 67.87 90 4 P1_1/c Kermesite Sb2S2O 8.147 10.709 5.785 102.78 110.63 101.00 4 P-1 
 

Table C12. Thermodynamic Properties of Selenides and Antimony Minerals 

CPr 0

cal mol-1 K

Mineral name Formula ΔGf ,Pr ,Tr 0

cal mol-1 

Ref ΔHf ,Pr ,Tr 0

cal mol-1

Ref SPr ,Tr 0

cal mol-1 K-1 

Ref VPr ,Tr 0

cm3 mol-1 

Ref 

a b ×103 

Naumannite Ag2Se -11998 -10397 35.813 35.768 19.479 Berzelianite Cu2Se -17089 -15607 30.914 28.846 19.460 Cadmoselite CdSe -33830 -34600 19.900 33.727 11.190 2.23 Clausthalite PbSe -23566 -24092 23.827 34.583 11.29 2.39 Tiemannite HgSe -9130 -10397 23.995 33.905 11.70 3.70 Ferroselite FeSe2 -19598 -21511 13.011 30.003 16.871 Kullerudite NiSe2 -25096 -26052 24.138 32.206 18.028 Antimony Sb 0. 0. 10.773 18.188 6 2.490 5.930 Senarmonite Sb2O3 -151400 27.370 52.220 6 22.530 12.030 Valentinite Sb2O3 -149600 29.430 49.874 6 24.840 10.300 Stibnite Sb2S3 -35846 43.499 73.352 6 28.750 3.927 Antimonselite Sb2Se3 -30019 -30497 48.911 82.441 6 28.38 5.00 Gudmundite FeSbS -109433 5 -109060 5 106.000 5 29.971 6 126 Berthierite FeSb2S4 -61176 58.560 91.261 6 46.590 0.2630 Kermesite Sb2S2O 8 8 8 66.282 6 

Notes 1. Zotov et al. (2003); 2. Xiong (2003); 3. Mills (1974); 4. Robie (1967); 5. Williams Jones and Normand (1997); 6. Calculated from cell parameters. See 

Thermodynamic properties for kermesite require calculation based on data reported by Williams Jones and Normand (1997), which was in turn based on earlier 

in this study to calculate Log K(kermesite) directly. 
 C24 of 32 

C5. Iron 

C5.1 Revision of Fe2+ and Fe3+

Uncertainties relating to the thermodynamic properties of the ferrous and ferric ions in aqueous 

solution have bedeviled precise modeling of geochemical systems involving these species for 

some time, e.g. see Nordstrom (1984) and Reardon and Beckie (1987). Recent work on the 

solubility of siderite (Preis and Gamsjager, 2002) provides support for the validity of the 

properties of Fe2+ in the survey by Parker and Khodakovskii (1995). We therefore incorporated 

revised properties of Fe2+ and Fe3+ in SUPCRT92 and recalculated the solubility products of all 

participating minerals whose thermodynamic properties were derived from calorimetry. These 

included chlorite, daphnite, pyrite, goethite and hematite, as well as appropriate corrections to 

the solubility products of siderite and ankerite. 

Calculations for generating the HKF EoS parameters for Fe2+ and Fe3+ were conducted with the 

aid of an Excel spreadsheet. In order to independently check the correctness of the transcribed 

equations, calculations were repeated for both species using theCPr ,Tr

0

 and SPr ,Tr

0

 cited by Shock 

et al. (1997). The derived values for the Born coefficient, ωPr ,Tr

, and c1, which depends on the 

value of , did not match closely with those cited by Shock et al. (1997). Agreement was obtained 

only if re, j,Pr ,Tr

for each species was assumed to have a discrete value of 2.64 and 3.56 A

o

, 

respectively. Furthermore, instead of using 

α z = 71.5 Z j , 

the original intercepts were used for the regressed data for ΔSn, j

o

− Z jΔSs,H + abs

o

(cal mol-1 K-1) for 

aqueous species with Zj = 1, 2 or 3 against Z j

2

re, j ( A

o

), where α z = 72, 141 and 211 cal mol-1 K1

, respectively. After appropriate substitution, close agreement was obtained, as shown in Table 

C13. 
 C25 of 32 

Table C13. Comparison of HKF EoS Parameters for Fe2+ and Fe3+ 

Parameter Shock et al. (1997) This Work 

Fe2+ Fe3+ Fe2+ Fe3+

c1, cal mol-1 K-1 14.7860 19.0459 14.7863 19.0462 

c2, cal K mol-1 x 10-4 -4.6437 -6.8233 -4.6438 -6.8234 

ωPr ,Tr

, cal mol-1 x 10-5 1.4382 2.5812 1.4382 2.5812 

 

In the present study, the calculated values for re, j,Pr ,Tr

given in Shock and Helgeson (1988) were 

used in the calculation of ωPr ,Tr

and c1. The a1 through a4 HKF equation of state parameters for 

Fe2+ and Fe3+ (Shock et al., 1997) were retained. The thermodynamic properties for both 

species are summarized in Table C14. 

Table C14. Thermodynamic Parameters for Fe2+ and Fe3+

Parameter Fe2+ Fe3+

ΔG f ,Pr ,Tr

0 , cal mol-1 -21637 -3891 

ΔH f ,Pr ,Tr

0 , cal mol-1 -21511 -11711 

SPr ,Tr

0 , cal mol-1 K-1 -24.283 -66.539 

CPr ,Tr

0 , cal mol-1 K-1 -7.37 -18.85 

V o

, cm3 mol-1 -22.2 -37.0 

a1, cal mol-1xbar-1 -0.7867 -2.4256 

a2, cal mol-1 -9.6869 -13.6961 

a3, cal K mol-1xbar-1 9.5479 11.1141 

a4, cal K mol-1 -2.3780 -2.2127 

c1, cal mol-1 K-1 15.2738 20.0173 

c2, cal K mol-1 x 10-4 -4.5359 -6.8743 

ωPr ,Tr

, cal mol-1 x 10-5 1.4574 2.6358 

C5.2 Iron Minerals 

Iron minerals are a particularly important factor in modeling the behavior of hazardous 

constituents in shallow groundwaters. Not only do they participate in buffering the redox 

potential of the system, but also by virtue of their adsorptive properties and their commonly very 

large specific surface areas (i.e., very fine crystallinity), they can be a significant reservoir of 

adsorbed hazardous constituents. Iron minerals of interest to the problem at hand consist of three 

broad classes: (1) Ferric/ferrous oxides and oxy-hydroxides, (2) silicates containing ferrous iron, 

particularly phyllites, and (3) sulfides. The recent literature was reviewed, and crystallographic 
 C26 of 32 

and thermodynamic data compiled, for a number of pertinent minerals, and the resulting 

information is summarized in Tables C15 and C16. 

Because the point of zero charge (PZC) of ferric oxides/hydroxides lies in the pH range of circa 

8.5, these minerals are generally positively charged over the pH range of the majority of shallow 

potable groundwaters, and would remain so upon intrusion of high-pressure CO2. The net 

positive charge attracts and adsorbs anionic species from solution. They are therefore likely to 

adsorb oxy-anionic species of As(III), As(V), Sb(III), Sb(V) and Se(II), Se(IV) and Se(VI). 

Other potential adsorbents include negatively charged sulfide and selenide complexes of heavy 

metals (Appendix D and Appendix E), although it is quite likely that sulfide and selenide species 

could be destabilized through oxidation of S(-II) and Se(-II) to higher oxidation states, depending 

on the overall equilibrium state. 

Two of the minerals listed are of particular interest: ferrihydrite and green rust. Ferrihydrite, a 

nanocrystalline or amorphous hydroxide, traditionally given the formula, Fe(OH)3, is commonly 

used in laboratory studies to quantify adsorption of charged or molecular species from solution, 

e.g., see the classic text on surface complexation modeling by Dzomback and Morel (1990). 

Until very recently, the structure of ferrihydrite was unknown. Indeed, it was commonly 

believed that ferrihydrite might consist of two separate phases (on the basis of X-ray diffraction 

studies) and thus referred to as “2-line” and “6-line” ferrihydrite. All this changed in 2007, when 

Michel and his co-workers (Michel et al., 2007a, 2007b; Penn, 2007), using advanced X-ray 

diffraction techniques and sophisticated methods of interpretation, were able to resolve the 

structure unambiguously as being isostructural with akdalaite, Al10O14(OH)2, more commonly 

known as tohdite (Shpanov et al., 1970; Tilley and Eggleton, 1994). With ferrihydrite now 

known to possess the stoichiometry, Al10O14(OH)2, calorimetric data for a series of ferrihydrites 

with differing specific surface areas and quantities of adsorbed water, (Majzlan et al., 2004) 

could be re-evaluated, and the enthalpy of formation, ΔHf ,Pr ,Tr

0 , for coarsely crystalline 

ferrihydrite calculated. Figure C3 shows a plot of measured ΔHf ,Pr ,Tr

0 , vs. excess water, 

extrapolated to zero excess water, the value at the intersection of the regression trendline with the 

y-axis being ΔHf ,Pr ,Tr

0 reported in Table C16. 
 C27 of 32 

y = -69.081x - 1017.4

R2

 = 0.9993

-2000

-1800

-1600

-1400

-1200

-1000

-800

-4 -2 0 2 4 6 8 10 12 14

Excess Water

Fig. C3. Correlation of calorimetrically determined enthalpy of formation of ferrihydrite samples in relation to 

their content of excess water. Data from Majzlan et al. (2004) 

Green rust is a mineral consisting of stacked layers separated by coordinated water molecules of 

Fe(II) tri-octahedral sheets, one of the basic building blocks of phyllosilicates or clay minerals. 

The octahedral sheet can be either of the gibbsite type, Al2(OH)6, or of the brucite type 

Mg3(OH)6, representing di- and tri-octahedral sheets, respectively. Partial in situ oxidation of the 

Fe(II) to Fe(III) in a tri-octahedral sheet causes the octahedral sheet to develop a net positive 

charge, which is satisfied through the uptake of anionic species in the inter-layer sites. The 

charge-balancing anion is commonly SO4-2, but any of the oxy-anionic species of As(III), 

As(V), Sb(III), Sb(V), Se(II), Se(IV) and Se(VI), as well as HCO3- or CO3-2 can also occupy 

sites. Because of their structural similarity to clays, such phases are sometimes referred to as 

“anionic” clays in distinction to regular “cationic” clays. In Tables C15 and C16, the properties 

of a typical idealized anionic clay, green rust, is given, where the stoichiometry of the waters of 

hydration were adjusted to fully saturate the interlayer positions. 
 

Table C15. Crystallographic Properties of Iron Minerals, excluding those containing Arsenic 

Name Formula Cell Constants 

ao (Å) bo (Å) co (Å) α β γ Z 

Space Group 

Goethite FeO(OH) 4.5979 9.9510 3.0178 90 90 90 4 Pbnm Lepidocrocite FeO(OH) 12.4 3.87 3.06 90 90 90 4 Bbmm Hematite2 Fe2O3 5.038 13.772 6 R-3c Maghemite Fe2O3 8.33 90 90 90 12 Fd3m E-Fe2O3 Fe2O3 5.095 8.789 9.437 90 90 90 8 Pna21 Marcasite FeS2 4.4446 5.4246 3.3864 90 90 90 2 Pnnm Pyrite FeS2 5.4166 90 90 90 4 Pa3 Ferrihydrite Fe10O14(OH)2 ~5.95 ~9.06 90 90 90 1 P63mc Mackinawite FeS 3.6735 5.0328 90 90 90 2 P4/nmm Greigite Fe3S4 9.876 90 90 90 8 Fd3m Schwertmannite Fe(III)16O16(OH)12(SO4)2 10.66(4) 6.04(1) 90 90 90 1 P4/m (?) Green Rust Fe(II)4Fe(III)2(OH)(SO4).8H2O 5.524 11.011 0.5 P3m1 

Table C16. Thermodynamic Properties of Iron Phases (Excluding those containing Arsenic and Carbon) 

CPr 0 c

Mineral name Formula 

ΔGf ,Pr ,Tr 0

cal mol-1 

Ref ΔHf ,Pr ,Tr 0

cal mol-1

Ref SPr ,Tr 0

cal mol-1 K1 

Ref VPr ,Tr 0

cm3 mol-1 

Ref 

a 

Goethite FeO(OH) -117065 5 14.269 4 19.866 9 0.289 Lepidocrocite FeO(OH) -114747 5 15.559 4 22.108 10 14.283 Hematite Fe2O3 178155 20 -197720 20.940 20 30.384 11 23.490 Maghemite Fe2O3 -173972 5 22.228 4 29.007 12 25.526 E-Fe2O3 Fe2O3 -171558 6 -190727 6 22.228 6 31.811 13 25.526 Marcasite FeS2 -37859 23 -40511 23 12.882 23 24.584 26 17.880 Pyrite FeS2 -38293 20 -41000 20 12.650 20 23.926 14 17.880 Ferrihydrite Fe10O14(OH)2 -799730 6,7 -1017400 101.800 3 167.280 15 124.560 Mackinawite FeS -22300 22 -21900 24 15.4 24 20.450 16 12.000 FeS,am FeS -21300 22 -20900 24 15.4 24 12.000 Greigite Fe3S4 -69400 22 -70300 24 47.1 24 72.511 17 42.000 Schwertmannite Fe(III)16O16(OH)12(SO4)2.12H2O -2661124 6,8 - 254.950 3 413.335 18 395.855 Green Rust Fe(II)4Fe(III)2(OH)(SO4).8H2O -1330280 1 - 192.870 3 350.466 19 220.065 Notes 1. Hansen et al. (1994), modified to account for 8H2O of water; 2. Calculated using the summation of oxide components; 3. Calculated using the procedure de

work. 4. Majzlan et al. (2003a); 5. Majzlan et al. (2003b); 6. Majzlan et al. (2004); 7. Data reinterpreted. See text; 8. Modified to account for 12H2O of 

Wyckoff (1963); 11. Blake et al. (1966); 12. Pecharroman et al. (1995); 13. Tronc et al. (1998); 14. Bayliss (1977); 15. Michel et al. (2007); 16. Lennie et al. 18. Bigham et al. (1994); 19. Simon et al. (2003); 20. As reported by Helgeson et al. (1978); 21. Assumed the same as for maghemite, in conformity with assu

(2004); 22. Berner (1967); 23. Robie and Hemingway (1995); 24. Estimates provided by Anderko and Shuler (1998); 25. Assumed to be equivalent to 
 C29 of 32 

C6. Carbonates 

An essential requirement for reactive transport simulations involving CO2 sequestration is the 

need for accurate thermodynamic data of participating carbonate phases. Recent studies have 

revealed respectively the possible existence of substantial errors in the Gibbs free energy of 

formation of magnesite and dolomite (Rock et al., 2001). Furthermore, the experimental 

consistency of siderite solubility data (Preis and Gamsjager, 2002) has been recently established, 

and by inference, validated the thermodynamic data for ferrous and ferric ions recommended by 

Parker and Khodakovskii (1995). These modifications also require a redetermination of the 

thermodynamic properties of the ankerite solid solution utilized in earlier work (Xu et al., 2003). 

The thermodynamic properties of dolomite and magnesite were calculated, using the Gibbs free 

energy values determined by Rock et al. (2001), together with the respective entropies provided 

by Robie and Hemingway (1995). These, together with entropy data for end-member ankerite, 

CaFe(CO3)2, from Holland and Powell (1998), and the properties of siderite from Preis and 

Gamsjager (2002) as well as calcite from the SUPCRT92 database, were used to calculate the 

properties of end-member ankerite and the ankerite ideal binary solid solution with the limiting 

composition: CaMg0.3Fe0.7CO3)2, using the lattice energy calculations reported by Chai and 

Navrotsky (1996). The thermodynamic data for carbonates, either calculated as described above 

or utilized in the present study, are summarized in Table C17. 
 

Table C17. Thermodynamic Properties of Selected Carbonate Minerals 

C

cal m

Mineral name 

Formula ΔGf ,Pr ,Tr 0

cal mol-1 

Ref ΔHf ,Pr ,Tr 0

cal mol-1

Ref SPr ,Tr 0

cal mol-1 K-1 

Ref 

VPr ,Tr 0

cm3 mol-1 

Ref 

a b ×1

Calcite CaCO3 -269880 1,2 -288552 1,2 22.150 1 36.934 1 24.980 5Magnesite MgCO3 -240791 4 -260816 6 15.557 3 28.018 1 19.731 12Siderite FeCO3 -162261 5 -179732 5 22.818 5 29.378 1 11.630 26Dolomite CaMg(CO3)2 -513341 4 -552250 6 37.089 3 64.365 1 41.557 23Ankerite Ca(Mg0.3Fe0.7)(CO3)2 -457553 6 -494243 6 43.627 6 65.594 6 46.070 18

Notes 1. Helgeson et al. (1978); 2. Helgeson (1990), Gibbs free energies calculated from solubility data reported by Plummer and Busenberg (1982) [Enthalpies adjus

Gibbs free energy]; 3. Robie and Hemingway (1995); 4. Rock et al. (2001); 5. Preis and Gamsjager (2002); 6. This work. See text. 
 C31 of 32 

C7. Chlorite 

In prior simulations (Xu et al., 2002), chlorite was represented as a 1:1 mixture of clinochlore 

and daphnite. The solubility products used were those derived originally from the 

thermodynamic evaluation of phase equilibria by Helgeson et al. (1987). More recent and 

comprehensive evaluations, e.g., those conducted by Holland and Powell (1998), indicate that a 

revision in the solubility products of clinochlore and daphnite is called for. Although clinochlore 

and daphnite are only minor components in most sedimentary arenaceous rocks, a revision is 

justified because they compete with secondary carbonates for magnesium and ferrous iron, and 

they can buffer the redox potential when coexisting with pyrite. 

The calculated thermodynamic properties for clinochlore and daphnite were taken from Holland 

and Powell (1998). The thermodynamic properties of chlorite, Mg2.5Fe2.5Al2[Si3O10](OH)8, were 

calculated assuming ideal solid solution between the end members. These properties were 

entered into the SUPCRT92 database, and the chlorite solubility products were calculated. The 

thermodynamic data used in the current study are given in Table C18. 

Table C18. Thermodynamic Parameters for Chlorite Minerals 

Parameter Units Clinochlore 

Mg5Al2[Si3O10](OH)8

Ref. Daphnite 

Fe5Al2[Si3O10](OH)8

Ref. Chlorite 

Mg2.5Fe2.5Al2[Si3O10](OH)8

Ref. 

ΔGf ,Pr ,Tr

0 cal mol-1 -1975051 4 -1562107 4 -1768992 4 

ΔHf ,Pr ,Tr

0 cal mol-1 -2134288 1 -1709845 1 -1922066 4 

SPr ,Tr

0 , cal mol-1 K-1 98.112 1 130.258 1 115.562 4 

VPr ,Tr

0 , cm3

 mol-1 207.110 1 213.420 1 210.260 4 

CPr

0 Coefficients 

a 166.500 2 176.210 2 171.355 3 

b ×103 42.100 2 43.760 2 42.930 3 

c ×10−5

cal mol-1 K-1

-37.470 2 -33.820 2 -35.645 3 

 Notes 1. Holland and Powell (1998); 2. Helgeson et al. (1978); 3. Interpolated between clinochlore and daphnite; 4. This work. 

C8. Miscellaneous 

The mineral hemimorphite proved to be an important thermodynamic control on the 

concentration of dissolved zinc in oxidized groundwaters, and might also control in part the 
 C32 of 32 

concentration in reducing groundwaters containing sphalerite. Thermodynamic data are 

extremely limited, essentially confined to preliminary study by McPhail et al. (2006), who 

measured the solubility at 50 and 80oC and computed the solubility product at the same 

temperatures. In Figure C4, a linear extrapolation of Log K(hemimorphite) is made as a function 

of reciprocal temperature to 25oC. The resulting value, Log K = 23.981, incorporated in the 

EQ3/6 and TOUGHREACT thermodynamic databases. 

y = 7.6212x - 1.5817

19.5

20

20.5

21

21.5

22

22.5

23

23.5

24

24.5

2.8 2.9 3 3.1 3.2 3.3 3.4

1/ T x 10 0 0 ( K^- 1)

LogK (hemimophite)

Fig. C4. A linear extrapolation of the solubility product of hemimorphite to 25oC with reciprocal temperature, 

1/K. Data from McPhail et al. (2006). 
D1 of 22 

Appendix D: 

Solubilities of Sulfides of Cadmium, Mercury, Lead 

and Zinc at 25°C 

D1. Background

The present study requires the calculation of the solubilities of minerals containing hazardous 

inorganic constituents as essential components under conditions that might occur in shallow 

potable water aquifers. The majority of the more than 38,000 groundwaters downloaded from 

the NWIS database and evaluated during the course of the present investigation appear to be 

reducing, and it is probable that pyrite or some other iron sulfide is present. Under such 

conditions, log PH2S (bar) ranges from approximately –8 to –10, and among the minerals 

potentially controlling the solubility of Zn, Cd, Hg and Pb in solution are the corresponding 

sulfides sphalerite, greenockite, cinnabar and galena. The divalent cations of these four elements 

can also complex with sulfide species (HS- and H2S) and selenide species (HSe- and H2Se) in 

solution, potentially enhancing both sulfide and selenide mineral solubilities. 

Among the sulfide and presumably selenide complexes in solution at equilibrium with the 

respective sulfides and selenide minerals are a series of neutral species of undefined 

stoichiometry, but circumstantial evidence suggests that they may be oligomers possessing 

formulas MexSx, where x = 1,2,3,4... Experimental evidence also suggests that these species are 

predominant under equilibrium conditions with the metal sulfide mineral, and thus must be taken 

into account if thermochemical data pertaining to sulfide mineral equilibria with respect to the 

aqueous phase are to be reconciled and mineral sulfide equilibria are to be modeled realistically. 

For this reason, both the identities of sulfide complexes of the cations of and their respective 

dissociation constants needed to be incorporated in the thermodynamic databases of both EQ3/6 

and TOUGHREACT codes. Without such data, simulations of reactive chemical transport of 

these metals in the presence of elevated PCO2 would not be representative. 

In this appendix, we review the issues concerning identification of the relevant sulfide 

complexes, and how their dissociation constants, Kdiss, relate to the solubility products, Ks, of the 
D2 of 22 

corresponding sulfide minerals, using galena (PbS) as an example. In the companion Appendix 

E, we review issues relating to the corresponding selenide complexes. Then, on the basis of a 

literature review, we choose and describe internally consistent data sets of sulfide and selenide 

species dissociation constants and associated solubility products for incorporation in the EQ3/6 

and TOUGHREACT databases. 

The procedure followed was first to generate a correlation plot for Log K(MeS)s and Log 

K(MeS)aq for sulfide species (Me = metal). A correlation plot was also generated comparing 

solid phase sulfide and selenide species, together with Hg sulfide and selenide species (Appendix 

E). The latter plot then allowed prediction of unknown neutral and charged selenide complexes 

of the relevant heavy metals based upon the identities and literature values of aqueous sulfide 

complexes of Cd, Hg Pb and Zn, and neutral sulfide complexes estimated from the first 

correlation plot. The resulting data were then incorporated in the EQ3/6 database and EQ3 run 

using a representative groundwater composition, based on the distribution of heavy metal 

concentrations observed in NWIS groundwaters, taking into account possible analytical artifacts. 

Alternative hypotheses were tested until a satisfactory reconciliation of all parameters was 

achieved, and the calculated saturation indices for those heavy metal sulfides and selenides 

expected to be present, based on observed modes of occurrence, fell close to 0, i.e., within ± 2 

units. The interdependence of all parameters tightly restricted the ranges over which each 

parameter could be varied independently. Therefore, although the results presented in this 

appendix and the following Appendix E leave room for refinement, it is expected that such 

refinements would not alter substantially the findings presented in this report. 

 

D2. Issues 

In both the EQ3/6 and TOUGHREACT codes, mineral solubility is defined in terms of a 

dissolution equation relating the mineral to the “basis” species in solution, e.g., for galena, the 

reaction is: 

PbS + H+ = Pb+2 + HS- (D1) 

where Pb+2 and HS-

 are the basis species in this case, and the solubility product is defined as 
D3 of 22 

Ks = Pb+2

[ ] HS−[ ]

[ ] PbS H+

[ ] (D2) 

with [ ] denoting thermodynamic activities. The activity of the solid phase is unity for a pure 

substance. In addition, knowledge of the potential complexes of the basis species in solution is 

required. In the example given, these complexes modify the total aqueous concentrations of both 

Pb+2 and HS-. Elsewhere in this report (Section 3.5), we show that apart from sulfide and 

selenide complexes, other significant aqueous species affecting the saturation concentration of 

Pb in shallow potable aquifers include PbCO3(aq), PbOH+, Pb(CO3)2

-2 and PbCl+

 in addition to 

Pb+2. The corresponding dissociation constants for these complexes are also defined in terms of 

the basis species. At higher sulfide concentrations, not normally encountered in potable water 

aquifers, significant quantities of sulfide complexes with Pb+2 might also form, e.g., Pb(HS)2(aq) 

and Pb(HS)3

-

, as shown by Hemley (1953) and Giordano and Barnes (1979). It is under such 

conditions that experiments are sometimes conducted, either to measure mineral sulfide 

solubilities, or to characterize the dissociation constants of relevant sulfide complexes in 

solution, where the saturated mineral is used as a thermodynamic control. 

The solubility product, defined by Eqn. (D2) can also be calculated from independent knowledge 

of the Gibbs free energies, ΔGf ,Pr ,Tr

0 , of the participating species, just as the measured solubility 

can be used to calculate ΔGf ,Pr ,Tr

0 of the solid phase. ΔGf ,Pr ,Tr0 of the solid phase or mineral can be 

obtained from calorimetric, thermo- or electro-chemical, or phase equilibrium studies. For 

example, ΔGf ,Pr ,Tr

0 (galena) has been determined from electrochemical (galvanic cell) 

measurements (Kiukkola and Wagner, 1957), thermochemical measurements (Stubbles and 

Birchenall, 1959), and by high-temperature oxide-melt solution calorimetry (Deore and 

Navrotsky, 2006). In Table D1, the solubility products of four sulfide phases are cross-compared 

with the corresponding values of ΔGf ,Pr ,Tr

0 derived from various property measurements. 
Table D1. Gibbs Free Energy of Formation and Solubility Products of Selected Trace Metal Sulfides 

Source Notes ZnS (sphalerite) CdS (greenockite) HgS (cinnabar) 

ΔGf ,Pr ,Tr 0

kcal mol-1

logKs

at 25°C 

ΔGf ,Pr ,Tr 0

kcal mol-1

logKs

at 25°C 

ΔGf ,Pr ,Tr 0

kcal mol-1

logKs

at 25°C 

Δ

kc

Robie and Hemingway (1995) 

-47.71±0.36 -11.27±0.26 -34.92±0.31 -14.09±0.23 -9.73±0.65 -38.08±0.48 -23

Deore and Navrotsky (2006) 

-48.29±0.96 -11.69±0.70 -34.73±0.99 -13.95±0.73 -23

Dyrssen and Kremling (1990) 

-47.25±0.93 -44.55±0.44 

-10.93±0.68 

-8.95±0.32

(wurtzite) 

-33.97±0.93 -13.39±0.68 -10.85±0.55 

-11.67±0.55 

-38.9±0.4

(black) 

-39.5±0.4

(red) 

-21

Daskalakis and Helz (1993) 

 -48.005 -11.482 

Shea and Helz (1989) 

 

Daskalakis and Helz (1992) 

-33.93±0.35 -13.36±0.26 

Paquette and Helz (1995) 

 -7.85 -36.7 

Notes: 1. Bold face values are given or derived directly from the source reference. 

2. logKs is in reference to the dissolution reaction: MS(s) + H+ = M+2 + HS-. Where M is Zn, Cd, Hg or Pb. 

3. ΔGf ,Pr ,Tr 0 values referenced to Deore and Navrotsky (2006) were calculated from ΔHf ,Pr ,Tr 0 assuming thatTΔSf ,Pr 0 corresponds to 

and Hemingway (1995) 

4. Uncertainties in derived values are calculated assuming no uncertainty in values of the aqueous species, and are therefore 

 minimum values. 5. ΔGf ,Pr ,Tr 0 values given by Robie and Hemingway (1995) are from Schaefer and Gokcen (1982) for sphalerite; Adami and Kin

Pankratz and King (1987) for cinnabar; Stubbles and Birchenall (1959) and Kiukkola and Wagner (1957) for galena. 
D5 of 22 

Most ΔGf ,Pr ,Tr

0 values calculated from solubility measurements in Table D1 are less negative than 

those determined using other methods. However, the thermochemical determinations 

summarized by Robie and Hemingway (1995) agree with the calorimetric determinations by 

Deore and Navrotsky (2006) within experimental error. Thus, it appears that solubility 

measurements may not adequately describe the speciation or thermodynamic behavior of the 

system. Tossell (1999), in his paper advocating ab initio quantum mechanical methods to 

calculate the stabilities of Hg complexes in solution, summarized the serious limitations of 

corresponding experimental methods: 

(1) “The speciation models are often ambiguous, with different researchers obtaining apparently 

different sets of species and formation constants.” 

(2) “... chemical components[,] whose activities or concentrations cannot be varied 

experimentally[,] are indeterminable, e.g., the participation of water in such species cannot 

be determined in aqueous solution. Spectral studies could, in principle, determine which 

species were present, as well as their concentrations if relative spectral intensities for the 

different species could be assessed. However, in many cases the species concentrations are 

so low that no usable spectra can be obtained.” 

Tossell, in particular, singled out Hg sulfide speciation as a case in point, but such problems also 

extend to the other metal sulfide species considered in this appendix. In a more recent review, 

Rickard and Luther (2006) reiterate the limitations of current experimental methods, noting that 

“One of the astonishing things the uninitiated reader will discover... is the large number of 

sulfide complexes that have been proposed and even modeled with little or no evidence to 

support their existence in the first place.” Given the current uncertainties in the literature, it is 

prudent to regard the following analysis with caution, even though the results presented are 

sufficiently robust to allow their use in the evaluation of thermodynamic controls on heavy metal 

solubilities in potable groundwaters. 

Explanations for the mismatch between the calculated and measured aqueous solubilities of 

chalcogenides in general has been accommodated by postulating equilibrium with a neutral 

species in solution, possessing the same stoichiometry as that of the solid phase, thus: 

MeS(s) = MeS(aq) (D3) 
D6 of 22 

where [MeS(aq)]/[MeS(s)] = Kint, the intrinsic solubility product (Dyrssen, 1985, 1988; Dyrssen 

and Wedborg, 1989, 1991; Benoit et al., 1999; Rickard, 2006). The precise nature of the 

complex is unknown. Dyrssen and Wedborg (1991) argue convincingly that it could not be a 

monomer. They point out that HgS(aq) is equivalent to the hydrated species, Hg(OH)(SH)(aq), 

and one cannot be distinguished from the other using conventional speciation studies (Tossell, 

1999; vide supra), i.e., 

HgS(aq) + H2O = Hg(OH)(SH)(aq); Log K is undetermined. (D4) 

Although the two stoichiometries cannot be distinguished in solubility studies, it is reasonable to 

assume that the complex is composed of (OH)- and SH- rather than S-2 and H2O. Dyrssen and 

Wedborg (1991) showed, using correspondence plots for Hg stability constants, and theory 

relating to calculation of mixed ligand complexes (Anfalt et al., 1968), that the calculated 

dissociation constant for the latter species is 

Hg(OH)(SH)(aq) + H+ = HgSH+ + H2O; Log K = +3.4. (D5) 

This dissociation constant is comparable with those of other hydrolytic reactions, e.g., 

Hg(OH)2(aq) + H+ = HgOH+ + H2O; Log K = +2.6 (D6) 

HgCl(OH)(aq) + H+ = HgCl+ + H2O; Log K = +3.1 (D7) 

However, the measured dissociation constant for 

HgS(aq) + H+

 = HgSH+; Log K ≈ -8.9 (D8) 

differs by approximately 12 log units. Similar incompatibilities were also recognized in 

computing the intrinsic solubility products for ZnS and CdS, based respectively on the work by 

Gubeli and Ste-Marie (1967) and by Ste-Marie et al. (1964). 

More recently, Rickard (2006), in studying the solubility of mackinawite (FeS), also identified 

and determined experimentally the intrinsic solubility product for this mineral 

FeS(mackinawite) = FeS(aq) (D9) 
D7 of 22 

He concluded that the FeS(aq) complex was an oligomer, citing earlier electrochemical studies 

by Buffle et al. (1988) and Theberge and Luther (1997) of FeS(aq) in equilibrium with 

amorphous FeS. The exact stoichiometry of FeS(aq) is uncertain. Theberge and Luther (1997) 

concluded that polymerization occurred as a two-step process, in which the octahedrally 

coordinated hex-aquo Fe+2 ion reacted with HS-

, thus, 

Fe(H2O)6

+2 + HS-

 = Fe(H2O)5(HS)+ + H2O (D10) 

followed by polymerization to form a trimer 

3Fe(H2O)5(HS)+

 + 3HS- = Fe3S3(H2O)6(aq) + 3H2S + 9H2O (D11) 

Implicit in this reaction is a transformation from octahedral to tetrahedral coordination in the 

ferrous ion, and formation of a closed six-member ring representing a basic building block in 

both cubic FeS and in mackinawite (where Fe(II) is tetrahedrally coordinated in both). However, 

it should be noted that the probability of the simultaneous reaction of six aqueous entities is 

extremely unlikely. Therefore Eqn. D11 must be considered merely a summary reaction that 

incorporates a series of intermediate steps, including the formation of a precursor dimer, and that 

it is probable that equilibria between the postulated trimer and its precursors is established at 

saturation with respect to the solid phase. 

A further comparison can be made with sulfide complexes of As and Sb, which form ionizing 

oligomeric complexes H3As3S6(aq) and H2Sb2S4(aq), respectively. These complexes apparently 

form to the exclusion of simpler monomeric species. Although both As and Sb are present in 

these complexes in the (III) state, a parallelism with the formation of postulated analogous 

oligomers with divalent (II) metals is suggested by the limited experimental evidence regarding 

the formation of Fe3S3(H2O)6(aq). 

Similar arguments could be advanced in defining polymer formation with respect to Zn and Cd 

sulfide complexes, where both Zn and Cd are tetrahedrally coordinated in both isostructural 

polymorphs, i.e., cubic sphalerite and hawleyite, and hexagonal wurtzite and greenockite, 

respectively. Hg(II) is tetrahedrally coordinated in cubic met cinnabar, but in cinnabar it is 

octahedrally coordinated. A similar coordination change is also noted in the sulfides of 

manganese (MnS). For example, in alabandite, Mn is octahedrally coordinated, whereas in 
D8 of 22 

ramsbergite, it is tetrahedrally coordinated in an anti-wurtzite structure. Thus, it is unclear 

whether Hg(II) aqueous sulfide complexes are octahedrally or tetrahedrally coordinated. 

Because metacinnabar is less stable than cinnabar, it is more likely that Hg(II) is octahedrally 

coordinated in sulfide complexes. With octahedral coordination, as also occurs with respect to 

Pb in galena, it would be logical to infer that the aqueous complexes are dimers, 

Me2S2(H2O)x(aq), or tetramers, Me4S4(H2O)y(aq) (where Me is either Hg or Pb), forming single 

or double four-member rings respectively. However, Rozen et al. (2003), on the basis of mass 

spectrometry of freeze dried aqueous samples, indicated that the species had the formula Pb3S3. 

D3. Estimation of the Dissociation Constants of 

Neutral Sulfide Complexes 

If a comparison is made between the sulfide solubility products and the dissociation constants of 

the neutral oligomeric sulfide complexes for the four metals for which data are available (Fe(II), 

Zn(II), Cd(II) and Hg(II)), conventionally written as 

MeS(s) + H+ = Me+2 + HS- (D12) 

and 

1/x[MexSx(aq) + xH+ = xMe+2 + xHS-] 

i.e., 

MeS(aq) + H+

 = Me+2 + HS- (D13) 

where x is probably 2, 3 or 4 (depending on the nature of the metal ligand), a surprisingly good 

correlation between the two is obtained, as shown by regression trendline (a) in Figure D1. 
D9 of 22 

Table D2. A Comparison between the Solubility Product of a Sulfide Mineral, and the Apparent Dissociation 

Constant of the Corresponding Neutral Species in Solution at 25°C 

Mineral Composition Log K 

(MeS)s

Source Log K 

(MeS)aq

Source 

Mackinawite FeS -3.50 1 +2.20 1 

Sphalerite ZnS -11.27 2 -5.63 3 

Greenockite CdS -14.09 2 -7.24 3 

Cinnabar HgS -38.08 2 -28.08 4 

Galena PbS -14.87 2 -8.06 5 

Notes: 

1. Rickard (2006) 

2. Derived from thermochemical data provided by Robie and Hemingway (1995) 

3. Dyrssen and Wedborg (1991) from data reported by Gubeli and Ste-Marie (1967) and Ste-Marie et al. (1964) 

4. Dyrssen and Wedborg (1989) 

5. This work. 

However, despite the impressive correlation between Log K(MeS)s and Log K(MeS)aq, 

incorporation of the dissociation constants of neutral sulfide and corresponding selenide 

complexes in the database did not satisfactorily resolve the unrealistic predictions of sulfide and 

selenide mineral saturation indices in NWIS potable waters. 
D10 of 22 

Fig. D1. Correlation between the solubility product of a sulfide mineral, and the apparent dissociation constant 

of the corresponding neutral species in solution at 25°C. 

An alternative approach at estimating the correlation between the solid and aqueous sulfides and 

selenide species was then examined. Dyrssen (1988), citing earlier work (Dyrssen, 1985), 

attempted to correlate the stability constants for sulfide complexes of a range of heavy and 

transition metals with the corresponding aqueous extraction constants, Kex , for dithizone 

dissolved in carbon tetrachloride: 

Men+ + n H2Dz(org) = Me(Hdz)n(org) + n H+

 (D14) 

He used studies by Ste-Marie et al. (1964) and Gubeli and Ste-Marie (1967) on Cd and Zn 

sulfide complexes, respectively, to calibrate the correlation equation: 

logK(MeS)aq = −0.95logKex − A (D15) 

where logK(MeS)aq represents Eq. (D13), and A = 3.02. Table D3 summarizes the respective 

parameters cited in Dyrssen (1988, 1989) together with other data used in the current analysis. 

When the data given in Table D3 are plotted, both Pb and Zn deviated significantly from the 
D11 of 22 

observed trend, although the data for Cd and Hg yielded satisfactory results for both sulfides and 

selenides (greenockite and cadmoselite, and cinnabar and tiemannite, respectively). Arbitrary 

revisions of the values of logK(MeS)aq to –5.5 and –3.5 for Pb and Zn, respectively, not only 

achieved better coherence with logK(MeS)aq values for other metals, but also yielded 

satisfactory saturation indices for galena, clausthalite, and sphalerite for the representative NWIS 

potable waters. The resulting regression trendline (b) is shown in Figure D1. For the purposes 

of this study, the logK(MeS)aq values for Cd, Pb and Zn summarized in Table D3 with the noted 

corrections for Pb and Zn are provisionally accepted for use in the databases for EQ3/6 and 

TOUGHREACT, and applied for the prediction of neutral selenide complexes of Cd, Pb and Zn. 

With respect to Hg, the value for logK(MeS)aq is chosen that was used in the construction of 

regression trendline (a) in Figure D1. This value is based on the intrinsic solubility product for 

HgS estimated by Dyrssen and Wedborg (1991), logKint −10. This value gave somewhat better 

agreement with postulated saturation with respect to cinnabar in NWIS potable waters. 

Table D3. Estimated Values of Neutral Sulfide Stability Constants, K MeS aq ( ) , based on the Corresponding 

Dithizone Extraction Constants, Kex , compared with Sulfide Solubility Products, K MeS S ( )

Metal logKex

1 logK(MeS)aq4 K MeS S ( )

Mn(II) -5.4 +2.1 -0.02 

Fe(II) -3.4 +0.2 -7.903

Ni(II) -0.8 -2.3 -9.503

Cu(II) 10.0 -12.5 -22.503

Zn(II) 2.0 -4.92 -11.27 

Cd(II) 1.9 -4.8 -14.09 

Hg(II) 26.8 -28.5 -38.08 

Pb(II) 0.4 -3.42 -14.87 

Notes: 

1. As cited by Dyrssen (1988) from Irving (1977) 

2. Adjusted to -3.5, -5.5 respectively for Zn and Pb. See text. 

3. Values cited by Dyrssen (1989). All other values for Log K(MeS), as given in Table D1. 

4. As cited by Dyrssen (1989) 

Despite the satisfactory agreement achieved through the correlation of logK(MeS)aq with logKex

proposed and used by Dyrssen in several of his papers, agreement is poor with respect to FeS 

from the more recent work of Rickard (2006). Furthermore, if logK(MeS)aq for Mn is correlated 

with logK(MeS)s using ΔGf ,Pr ,Tr

0 by Robie and Hemingway (1995), correlation of Mn(II) with 
D12 of 22 

the rest of the data set is also poor. Arbitrary correction of logK(MnS)aq to account for the 

relative difference between logKex for Fe(II) and Mn(II) does, however, restore a more restricted 

correlation, if the outlier for Hg is excluded and the values obtained by Rickard (2006) replace 

those given by Dyrssen (1989), as indicated for regression trendline (c) in Figure D1. Clearly, 

such speculations do little to improve the rigor of the analysis and are included merely to suggest 

further lines of inquiry. 

In the following paragraphs, we briefly describe the data sets for sulfide complexes of Zn, Cd, 

Hg and Pb with reference to the compilations in Tables D1 and D2, and to selected speciation 

models for each metal in the published literature. 

D4. Greenockite (CdS) 

The data for ΔGf ,Pr ,Tr

0 for greenockite indicates ≈ 1.00 kcal/mol discrepancy between those values 

obtained by calorimetry or thermochemical measurements, and those derived from solubility 

measurements. Davies-Colley et al. (1985), in comparing measured Cd+2 concentrations in 

relatively high p(HS-

) anaerobic estuarine sediments with those based on the assumed presence 

of greenockite precipitate, found that greenockite solubility was exceeded by about one log unit. 

The basis for their calculations was ΔGf ,Pr ,Tr

0 (greenockite) = -34.8 kcal/mol from an earlier 

publication by Robie et al (1978), which is similar to that given by Robie and Hemingway 

(1995). The sulfide speciation used in their calculations also differs from that given by 

Daskalakis and Helz (1992). Davies-Colley et al. (1985) attribute the discrepancy to the possible 

presence of polysulfide complexes, or that the solubility-controlling sulfide was a poorly ordered 

CdS phase. Given the inherent uncertainties in the interpretation of field data, and lack of 

knowledge as to the nature of immobile Cd+2 in the sediments, i.e., whether as a discrete solid 

phase, or in solid solution with sphalerite, or whether adsorbed on thiol groups on organic 

material, it is perhaps surprising that such good agreement was obtained. Table D4 presents the 

internally consistent data set for Cd sulfide complexes from the study by Daskalakis and Helz 

(1993). The Log K value for the dissociation of CdS(aq) is derived from data presented by 

Dyrssen and Wedborg (1991) as summarized in Table D4, and the solubility product is based on 

thermochemical data given by Robie and Hemingway (1995). 
D13 of 22 

 

Table D4. Complexes of HS- with Cd+2 after data by Daskalakis and Helz (1993), CdS(aq) after Dyrssen and 

Wedborg (1991) and Greenockite Solubility Product derived from Thermochemical Data given by 

Robie and Hemingway (1995), all at 25°C 

Reaction Log K 

Cd(HS)3

-

 = Cd+2 + 3 HS- -16.44 

Cd(HS)4

-2 = Cd+2 + 4 HS-

 -17.89 

Cd(OH)2(HS)- + 2 H+ = Cd+2 + 3 HS- + 2 H2O +2.47 

CdS(aq) + H+ = Cd+2 + HS- -4.8 

CdS(greenockite) + H+ = Cd+2 + HS- -14.09 

D5. Cinnabar (HgS) 

Discrepancies in derived values of ΔGf ,Pr ,Tr

0 (cinnabar) are substantial, ranging over almost 4 

kcal/mol, and the corresponding values of log Ks show a range of approximately 3 units (Table 

D1). The uncertainty is aggravated by questions concerning the identification of Hg sulfide 

species in solution and the magnitudes of their dissociation constants, c.f. Tossell (1999). We 

have identified and calculated the relevant dissociation constants using the results presented by 

Schwarzenbach and Widmer (1963) and modified by Paquette and Helz (1997) on the solubility 

of red cinnabar in buffered 0.7 M KCl solution at 25°C. In order to retrieve the dissociation 

constants from their data, a solubility product for cinnabar must be assumed, and the first 

dissociation constant for H2S(aq) specified. The solubility product derived from 

ΔGf ,Pr ,Tr

0 (cinnabar), given by Robie and Hemingway (1995) (Log K = -38.08), and the 

dissociation constant for H2S(aq) specified by Bessinger (2000) (Log K = -6.980) were used in 

the calculation. Corrections to zero ionic strength standard state conditions were not applied, as 

activity coefficient corrections are self compensating for two of the three species, and correction 

for the third would not be very accurate given the limited amount of available data. Results of 

the calculations are presented in Table D5. 
D14 of 22 

Table D5. Complexes of HS- with Hg+2 after data by Schwartzenbach and Widmer (1963) and modified by 

Paquette and Helz (1997), HgS(aq) after Dyrssen and Wedborg (1989) and Cinnabar Solubility 

Product derived from Thermochemical Data given by Robie and Hemingway (1995), all at 25°C 

Reaction Log K 

Hg(HS)2(aq) = Hg+2 + 2 HS- -39.65 

HgS(HS)- + H+ = Hg+2 + 2 HS- -32.67 

HgS2

-2 + 2 H+

 = Hg+2 + 2 HS- -23.91 

HgS(aq) + H+ = Hg+2 + HS- -28.08 

HgS(cinnabar) + H+ = Hg+2 + HS- -38.08 

Incorporation of the neutral HgS(aq) complex in the analysis is essential, as use of the cited 

solubility product for cinnabar and the dissociation constants of the Hg sulfide aqueous species 

alone would predict unrealistically low Hg concentrations in groundwaters. This difficulty was 

also recognized by Benoit et al. (1999), who incorporated HgS(aq) in their study of dissolved 

mercury behavior in sediments from the Florida Everglades and the Patuxent River Estuary of 

Maryland. Our value for Log K = -28.08 can be compared with their value of –26.5 and their 

assumed Log solubility product of cinnabar of –37.0 instead of –38.08 used here. Correction for 

the higher solubility product would yield a value of –27.6, which is in substantial agreement with 

our own value for this parameter. 

It should be noted that Benoit et al. (1999) did not assume that dissolved mercury had at any time 

equilibrated with cinnabar as a discrete solid phase in the sediments under study. Rather, they 

considered cinnabar as a potential solid solution component in a matrix of iron monosulfide or 

pyrite. Their treatment was analogous to their assumption that Hg+2 complexes with a “solid 

solution” of thiol groups on organic material. However, although saturation with respect to 

cinnabar was never observed, their model predicted quite well the variability of Hg concentration 

in sediment pore waters with dissolved sulfide concentration, and was independently correlated 

with expected methyl mercury speciation. Their model could therefore be considered a 

reasonable paradigm for testing the correlation of aqueous Hg concentrations with cinnabar 

saturation, even extrapolated to the much lower dissolved sulfide concentrations expected in 

potable groundwaters. 
D15 of 22 

D6. Galena (PbS) 

Issues relating to the solubility of galena in groundwaters are discussed at somewhat greater 

length than in previous sections for other sulfide minerals, because of its relevance in this 

project. Reported values for the solubility product of galena (PbS), as defined by the dissolution 

equation 

PbS + H+

 = Pb+2 + HS- (D16) 

vary over two orders of magnitude, as shown in Table D1. The value utilized by Wang and Jaffe 

(2004) in their simulations of galena dissolution in shallow potable water aquifers was taken 

from a database compiled for the MINTEQ code (Felmy et al., 1984; Hydrogeologic and Allison 

Geoscience Consultants, 1999), but also used by the PHREEQ-C code (Parkhurst and Appelo, 

1999). This database (minteq.v4.dat 85 2005-02-02 16:13:00Z dlpark), gives a logKs value of 

-13.97 at 25°C for the above reaction. The source of this value can be traced to Dyrssen and 

Kremling (1990), who in a study of trace metal behavior in anoxic Baltic waters attempted to 

derive Log Ks values for a number of metal sulfides. The values presented are based on the 

product of two reaction constants, K1, and KsO, representing respectively the reactions: 

H+

 + S2- = HS- (D17) 

and 

MaSb(s) = aMn+ + bS2- (D18) 

where M represents the metal with charge +2 (n=2, a=b=1) or +1 (n=1, a=2, b=1). Because of 

the high degree of uncertainty in K2 (published values of LogK1 vary between 12 and 

18.51±0.56), the authors utilized published values of KsO together with corresponding values for 

K1 from the original sources to calculate the product (K1 KsO) for the reaction: 

MaSb(s) + bH+

 = aMn+ + bHS- (D19) 
D16 of 22 

which for galena is represented by the reaction described in Eq. (D1). 

The PHREEQ-C code is also capable of using another database used by the WATEQ code 

(wateq4f.dat 431 2005-08-23 17:29:36Z dlpark). This database gives a logKs = –12.780 at 25°C 

for the reaction in Eq. (D1), which is 1.2 log units higher than the value cited by Dyrssen and 

Kremling (1990). The cited value is a correction by Shea and Helz (1989) for Pb complexation 

by chloride ion based on results from an earlier experimental study by Uhler and Helz (1984). 

The latter investigators enhanced the solubility of galena through Pb+2 complexing with ethylene 

diamine acetic acid (EDTA) in a solution of disodium EDTA and 0.1 M NaCl. The technique 

overcomes a serious difficulty in analyzing solution compositions where one or more of the 

principal chemical components are at or below the detection limits of available analytical 

methods, yet introduces additional complexity and uncertainty associated with the 

thermodynamic parameters needed to interpret the behavior of the chelating agents used in the 

study. The technique is also dependent on knowledge of the identities and thermodynamic 

properties of lead sulfide complexes in solution. Finally, derivation of Ks depends on 

appropriate ionic strength corrections to permit extrapolation to standard state conditions. 

Despite much progress in making such corrections, current techniques still leave room for 

improvement. 

The EQ3/6 code (Wolery et al., 1992) used in this study utilizes another thermodynamic database 

data0.ymp.R4 (downloaded N.S. 3/3/05 DTN: SN0410T0510404.002), which was qualified for 

use under the Yucca Mountain Project and supported by the U.S. Department of Energy. The 

database gives a logKs = -14.8544 at 25°C for the reaction in Eq. (1), which is 0.95 log units 

lower than the value cited by Dyrssen and Kremling (1990). This value was calculated from the 

thermodynamic properties for galena reported in Helgeson et al. (1978), who in turn cited Mills 

(1974). The reported value for ΔGf ,Pr ,Tr

0 , -23.12 kcal/mol, is close to the -23.14 kcal/mol reported 

by Robie and Hemingway (1995), which is in turn based on thermochemical measurements by 

Stubbles and Birchenall (1959). Stubbles and Birchenall (1959) conducted thermochemical 

measurements on lead/lead sulfide equilibria between 585 and 920oC, necessitating a substantial 

extrapolation to the 25oC reference temperature. Uhler and Helz (1984), in referring to the same 

work, note further that, apart from the magnitude of the extrapolation, additional uncertainties 

arise from phase changes in both elemental lead and sulfur between 585 and 25oC, contributing 
D17 of 22 

to an overall uncertainty in excess of 2 kcal/mol in ΔGf ,Pr ,Tr

0 (galena), or > ±1.5 units in LogKs. 

However, this uncertainty is much larger than that estimated by Robie and Hemingway (1995) 

for the same data (Table D1). 

Barrett and Anderson (1982, 1988) measured the solubility of both galena and sphalerite (ZnS) at 

temperatures between ≈25 and 95oC in sodium chloride brines ranging between 3 and 5 M. 

These investigators then independently calculated the solubilities of galena and sphalerite to 

300oC in sodium chloride brines between 1 and 5 M at pH = 4 and ms(r) = 0.001. They also 

compared their measured solubilities with the independently calculated values, and obtained a 

reasonable match (calculated values were between 0.3 and 0.7 log units too low). Their 

calculations were based on logKs(galena) cited in Bowers et al. (1985), which appears to have 

been derived from the sources cited by Helgeson et al. (1976). The calculations took into 

account Pb chloride complexing quantified by Seward (1984) and Ruaya and Seward (1986), and 

activity coefficient (γi) corrections were based on the b-dot method described by Helgeson 

(1969). Barrett and Anderson (1988) contrasted their results using the logKs(galena) cited in 

Bowers et al. (1985) with those obtained by Uhler and Helz (1984), noting the almost two order 

of magnitude discrepancy, and pointing out that using the latter would result in calculated 

solubilities much greater than their measured values. However, they also note that their own 

experimental measurements are subject to errors arising from inadequate corrections for liquid 

junction potentials in their measurements of pH. Furthermore, their calculated solubilities are 

subject to the same uncertainties associated with corrections for chloride and sulfide complexing 

by Pb+2. Inadequate corrections for activity coefficients of the ionic species in high-ionic 

strength sodium chloride brines are also of concern to the derived results of Uhler and Helz 

(1984), as noted above. 

In conclusion, the discrepancies between Ks(galena) derived from solubility measurements and 

those based on thermochemical measurements are so significant that meaningful predictions of 

galena solubility in shallow potable groundwaters at ambient temperatures are not possible 

unless the conflict is resolved. This conflict appears to be caused by failure to account for the 

presence of the oligomeric complex PbS(aq), by analogy with the resolved discrepancies 

between measured and calculated sphalerite, greenockite and cinnabar solubilities, given in the 

preceding sections. We therefore adopted the strategy described in the introduction of this 
D18 of 22 

Appendix, i.e., Log Ks(galena) would be based on thermochemical data, and the dissociation 

constant of PbS(aq) would be estimated based on the regression analysis between Log K(MeS)s

and logK(MeS)aq illustrated in Figure D1. The value chosen for Log (PbS)aq, -5.5, is consistent, 

within the overall uncertainty of the analysis, with independent measurements by Rozan et al. 

(2003), who give a value for LogKTHERM =16.83± 0.33 for PbS(aq), 36.38±0.47 for Pb2S2(aq), 

62.49±0.61 for Pb3S3, and 135.6±0.8 for Pb6S6, respectively, for the reaction 

xPb+2 + xS-2 = PbxSx(aq) (D20) 

Normalizing these LogKTHERM values to unit stoichiometry, as well as recasting the reaction in 

terms of HS-

 and as a dissociation yields LogK(PbS)(aq) = +0.31, -1.67, -4.46 and -6.08, 

respectively. Bearing in mind that Rozan et al. (2003), on the basis of mass spectrometry 

determinations of freeze-dried samples, found that the species Pb3S3(aq) appeared to 

predominate, it can be seen that there is reasonable agreement between the normalized log K 

value for that species, -4.46, and the value of –5.5 provisionally accepted for the average of all 

oligomeric PbS(aq) species. Note, however, that the foregoing calculation assumed 

LogKTHERM =16.83± 0.33 for the reaction 

S-2 + H+

 = HS- (D21) 

Values ranging from 18.51 ± 0.56 (Schoonen and Barnes, 1988) to ≈ 14.0 have been reported in 

the literature (Dyrssen, 1989). Therefore the uncertainties in the derived values for 

LogK(PbS)(aq) from Rozan et al. (2003) are substantial. 

The dissociation constants for other Pb sulfide species in solution are derived from the 

association constants reported by Giordano and Barnes (1979) at 30°C, which are essentially 

identical to earlier work by Hemley (1953) at 25°C, and were treated analogously to those for Hg 

sulfide complexes using solubility products derived from ΔGf ,Pr ,Tr

0 (galena), given by Robie and 

Hemingway (1995) (Log K = -14.870), and the dissociation constant for H2S(aq) specified by 

Bessinger (2000) (Log K = -6.98). The relevant data are given in Table D6. 
D19 of 22 

Table D6. Complexes of HS- with Pb+2 after data by Giordano and Barnes (1979), PbS(aq) computed in this 

Appendix, and Galena Solubility Product derived from Thermochemical Data given by Robie and 

Hemingway (1995), all at 25°C 

Reaction Log K 

Pb(HS)2(aq) = Pb+2 + 2 HS- -14.058 

Pb(HS)3

-

 = Pb+2 + 3 HS- -15.258 

PbS(aq) + H+ = Pb+2 + HS- -5.5 

PbS(galena) + H+ = Pb+2 + HS- -14.870 

D7. Sphalerite (ZnS) 

Excellent agreement is obtained for the calculated values of ΔGf ,Pr ,Tr

0 for sphalerite derived from 

the data by Daskalakis and Helz (1993), Deore and Navrotsky (2006), and Schaefer and Gokcen 

(1982). The value of ΔGf ,Pr ,Tr

0 derived from data by Dyrssen and Kremling (1990) is somewhat 

higher, but otherwise in reasonable agreement. Barrett and Anderson (1988), using a value of 

Log Ks for sphalerite of –11.40, found that the measured solubility at 25°C was 0.4 log units 

higher in 3 M NaCl brine, i.e., Log Ks ≈ –11.00, consistent with that of –10.93 reported by 

Dyrssen and Kremling (1990). Bearing in mind that Barrett and Anderson’s measured solubility 

measurements are difficult to interpret, as noted further in Section D5 of this appendix, the 

consistency of their results with other solubility measurements, and with thermochemical 

determinations of ΔGf ,Pr ,Tr

0 suggests that reasonable confidence can be attached to the internally 

consistent dataset for sulfide speciation and log Ks for sphalerite, obtained by Daskalakis and 

Helz (1992), and presented in Table D7, below. An uncertainty of about ± 1 log unit is expected 

in predictions of sphalerite saturation indices. The Log K value for the dissociation of ZnS(aq) is 

derived from data presented by Dyrssen and Wedborg (1991) as summarized in Table D7, and 

the solubility product is based on thermochemical data given by Robie and Hemingway (1995). 

 
D20 of 22 

Table D7. Complexes of HS- with Zn+2 after data by Daskalakis and Helz (1992), ZnS(aq) after Dyrssen and 

Wedborg (1991) and Sphalerite Solubility Product derived from Thermochemical Data given by Robie 

and Hemingway (1995), all at 25°C 

Reaction Log K 

Zn(HS)4

-2 = Zn+2 + 4 HS- -14.640 

ZnS(HS)- + H+ = Zn+2 + 2 HS- -6.842 

ZnS(HS)2

-2 + H+

 = Zn+2 + 3 HS- -6.152 

ZnS(aq) + H+ = Zn+2 + HS- -3.5 

ZnS(sphalerite) + H+ = Zn+2 + HS -11.27 

D8. Recommendations for Further Refinement of 

Thermodynamic Data Pertinent to Sulfide and 

Selenide Solubilities 

A pragmatic approach was taken in the foregoing analysis to reconcile thermochemical data for 

solid sulfide and selenide minerals with measured aqueous solubilities, and further reconcile 

field measurements of heavy metal concentrations in potable groundwaters with postulated 

saturation indices for the sulfide and selenide minerals. For reasons of time, an in-depth analysis 

was not conducted. 

In their review of metal sulfide complexes and clusters, Rickard and Luther (2006) cite studies 

suggesting the existence of several other charged and uncharged oligomeric metal clusters of Fe, 

Cu, Zn, Cd, Ag, Au, Pb and Hg. Supporting evidence is for the most part equivocal, and further 

definitive studies are needed to resolve current ambiguities regarding cluster stoichiometries and 

stabilities. The same authors also cite recent work by Zhang and Millero (1994), Al-Farawati 

and van den Berg (1999), and Rozan et al. (2003) all of whom used various voltammetric 

techniques to identify sulfide speciation in marine waters for a range of elements, including those 

of interest in this report. Unfortunately, time did not permit evaluation of their findings in 

relation to the work presented here. However, some preliminary checks indicate consistency 

with the results presented in this appendix. Further analysis of the data presented in these and 

other papers cited by Rickard and Luther (2006), as well as the work of ongoing investigations 

must be conducted as part of a broader strategy to refine the geochemical model for potable 

waters. 
D21 of 22 

 The following are aspects of the problem that were insufficiently addressed. 

(1) The aqueous species MeS(aq) represents a distribution of oligomeric complexes, which 

would vary depending on both intensive and extensive variables in the system. Molecular 

dynamic modeling and/or spectroscopic studies will be required to establish the identities of 

species and their thermodynamic relations. Recent studies of sulfide complexation in marine 

waters using voltammetric methods (vide supra) must be reviewed and compared with earlier 

work cited in this appendix and account taken of oligomeric species where their 

thermodynamic properties have been quantified. 

(2) Experimental measurements of sulfide and selenide mineral solubilities were conducted in a 

variety of supporting electrolytes. Ionic strength corrections to the activities of species were 

not uniformly treated or evaluated. 

(3) Corrections to mineral solubilities by assuming the presence of neutral sulfide complexes 

were made without a re-evaluation of the raw data. Consequently, corrections to the 

distributions between species such as MeS(aq) and species such as Me(HS)2(aq) were 

accounted for in a consistent manner. 

(4) Improved correlation plots between sulfide and selenide species might be achieved by 

consideration of experimental work on selenide complexes of Mn and Ag by Mehra and 

Gubeli (1970a and 1970c). 

(5) Changes in the distributions of sulfide and selenide minerals as a function of heavy metal 

abundances and redox potential in natural systems should be examined further to establish 

the phase relations of selenide minerals in relation to corresponding sulfide minerals in 

potable water aquifers. 

(6) In making tests of the validity of calculated saturation indices of sulfides and selenides in 

potable groundwaters, the effect of temperature was ignored. Potable groundwater 

temperatures could range down to as low as 5oC, which could shift the calculated saturation 

index by as much as one log unit. In situ temperatures of the studied NWIS potable waters 

should be retrieved and temperature corrections made for the dissociation constants of all 

participating aqueous species and all participating mineral solubility products. 
D22 of 22 

(7) The stability constants of mixed sulfide/selenide complexes should be estimated and 

incorporated in the thermodynamic database used in the evaluation of saturation indices of 

sulfide and selenide minerals in potable groundwaters. 
E1 of 7 

Appendix E: 

Dissociation Constants for Selenides of Arsenic, 

Cadmium, Mercury, Lead and Antimony 

E1. Background 

During the course of the current investigation, it became apparent that the concentrations of 

several hazardous inorganic constituents in groundwaters could be controlled by their selenides, 

rather than their corresponding sulfides. This supposition is reinforced by field evidence for the 

existence of clausthalite (PbSe) occurring in over 50% of coals in the Appalachian Basin and 

10% outside the Basin (Finkelman, 1981), and the occasional occurrence of tiemannite (HgSe) in 

coal (Tewalt et al., 2001). Furthermore, both cadmoselite (CdSe) and antimonselite (Sb2Se3) 

have been observed in sedimentary formations under conditions that might suggest their presence 

in potable groundwater aquifers. 

Preliminary calculations of the saturation indices for metal (Me) selenides, using solubility 

products based on thermodynamic data in reported in the published literature, for the reaction: 

MeaSeb(s) + bH+ = aMen+ + bHSe- (E1) 

also indicated that all of the cited selenides would be extremely supersaturated in those 

groundwaters from the NWIS database reporting Se together with Cd, Hg, Pb or Sb. In fact, the 

calculated saturation indices were so unrealistically high (≈ +5 to +10), that it was clear that that 

the thermodynamic database used in the EQ3/6 code for the evaluation was seriously deficient 

and failed to represent the behavior of selenide mineral solubilities. this deficiency would 

compromise the objective of the study to correctly identify and quantify the thermodynamic 

controls governing the concentrations of the cited constituents in potable groundwaters.

An obvious omission in the Data0.dat database were the selenide complex dissociation constants 

corresponding to those of sulfide complexes with As+3, Cd+2, Hg+2, Pb+2, Sb+3, Zn+2 and Fe+2, 

which were incorporated on the basis of a review of their dissociation constants given in 

Appendix D. A perusal of the literature lead to the discovery of two reviews regarding the 

solubilities of selenium phases and selenium speciation in solution, Cowan (1988) and Seby et al. 
E2 of 7 

(2001). Both reviews cited the same sources regarding selenide complexation with metal ions, 

which were limited to studies by Mehra and Gubeli (1970a,b, 1971) concerning Ag, Mn and Hg 

selenide complexation. No other studies relating to selenide complexation with metals in 

aqueous solution appear to have been published since date of the latest review. Only the 

information by Mehra and Gubeli (1971) concerning Hg selenide complexing is relevant to the 

current study. In the following paragraphs, we discuss how the available data were adapted to 

account for selenide complexation with As+3, Cd+2, Hg+2, Pb+3, Sb+3, Zn+2 and Fe+2. 

E2. Hg Selenide Complexes in Aqueous Solution 

Mehra and Gubeli (1971) independently determined the Log solubility product of HgSe, 

according to the reaction 

HgSe(s) = Hg+2 + Se-2 (E2) 

to be –56.6±0.2. Assuming Log K for the reaction 

HSe- = H+ + Se-2 (E3) 

is –14.0±1.0 (Seby et al., 2001), it follows that Log K(tiemannite) for the reaction 

HgSe(s) + H+

 = Hg+2 + HSe- (E4) 

is –42.6. This value is comparable with the value, -43.27, calculated from thermochemical data 

reported by Vaughan and Craig (1978), and the same as that reported by Mills (1974), which has 

been incorporated in the EQ3/6 Data0.dat, V4.2.YMP, and accepted in this report. 

The data presented by Mehra and Gubeli (1971) also allows calculation of the intrinsic solubility 

of HgS (tiemannite) under acid conditions (i.e., at pH < 3.8), as their measurements were 

conducted in 1 Normal NaClO4 solution assumed to be in equilibrium with solid HgSe. 

Assuming the reaction 

HgSe(s) =HgSe(aq) (E5) 
E3 of 7 

is in equilibrium with a total log concentration of Hg = -7.83, as reported by Mehra and Gubeli 

(1971), then LogKint = −7.83. Given that Log K(tiemannite) for the reaction (E2) is –43.27, the 

dissociation constant for the reaction 

HgSe(aq) + H+

 = Hg+2 + HSe- (E6) 

is LogKHgSe(aq) = −35.43. 

Instead of postulating the existence of HgSe(aq) as a proxy for oligomeric species in solution, 

Mehra and Gubeli (1971) assumed the existence of a complex of the form Hg(HSe)(OH)(aq), 

which, through subtraction of one H2O, assumes the same form as HgSe(aq). The two species 

are therefore equivalent, and it would be impossible to discriminate between the two on the basis 

of the experimental results alone. 

Mehra and Gubeli (1971) also postulated the existence of two other species in solution, 

Hg(HSe)2(OH)-

 and Hg(HSe)2(OH)2

-2, which are equivalent to HgSe(HSe)-

 and HgSe2

-2, 

respectively. 

Association constants for each were defined thus: 

β21 = Hg HSe ( )2 ( ) OH −

[ ]

Hg+2

[ ] HSe−[ ]2

OH−

[ ]= [ ] Hgcomplex K2

2

Ksp •KW H+

[ ] Se−2[ ] (E7) 

where Logβ 21 = 52.8 ± 0.3, and 

β22 = Hg HSe ( )2 ( ) OH 2

−2

[ ]

Hg+2

[ ] HSe−[ ]2

OH−

[ ]2 = [ ] Hgtot K2

2

Ksp •KW

2 Se−2

[ ] (E8) 

where Logβ 22 = 61.0 ± 0.3. The value of the second ionization constant, LogK2 = −11.6, had 

been determined earlier by Mehra and Gubeli (1970a). LogKsp = 56.6 ± 0.2 is the solubility 

product of HgSe, tacitly assumed to be tiemannite. It was also calculated from the value reported 

for the reaction 

HgSe = Hg+2 + Se-2 (E9) 
E4 of 7 

Log Kw is the dissociation constant for water, which was not specified. The log K2 value is 

inconsistent with that recommended by Seby et al. (2001) (log K2 = -14.0 ±1.0), the elevated 

value being possibly caused by inadvertent contamination by polyselenides generated through 

oxidation of HSe-

 by traces of oxygen (Wood, 1958), even though the study was conducted 

under an inert atmosphere of nitrogen. Because a 1 M NaClO4 solution was used as the 

supporting electrolyte, a more realistic value for log KW is –13.7, a value used by Dyrssen (1988) 

in correcting for the ionic strength of seawater. Finally, LogKsp differs from that calculated from 

thermochemical data by approximately 0.7 log unit, which might be due to a finer crystallinity of 

the material used in their solubility determinations. Because this discrepancy is within the 

overall uncertainty of the calculations, no correction was applied to account for this difference. 

Application of appropriate corrections yields revised values for the dissociation constants, which 

are presented in Table E1. 

Table E1. Dissociation Constants of Complexes of HSe- with Hg+2 and the Tiemannite Solubility Product in 

1 M NaClO4 Solution at 25oC (after data by Mehra and Gubeli, 1971). 

Reaction Log K 

HgSe(aq) + H+ = Hg+2 + HSe- -35.43 

HgSe(HSe)- + H+ = Hg+2 + 2 HSe- -39.10 

HgSe2

-2 + 2 H+

 = Hg+2 + 2 HSe- -28.80 

HgSe(s) + H+ = Hg+2 + HSe- -43.27 

Corrections were inconsistently applied to the reported dissociation constants for HgSe(HSe)-

and HgSe2

-2. The corrected value for Log K(HgSe(HSe)-

 ) should have been –34.30. However, 

had this value been used instead of that cited in Table E1, the calculated Log K values of 

selenide complexes given in this appendix would have differed very little from those actually 

calculated and used in subsequent calculations in this report. With respect specifically to Hg 

selenide complexation, the differences are also inconsequential, and well within the overall 

uncertainty of the model results, as the HgSe(HSe)-

 species is always less than 10-6 % of the 

total Hg in groundwaters. It should also be noted that the corrections are preliminary, and that a 

more careful re-evaluation of the findings by Mehra and Gubeli (1971) is required. Furthermore, 

it is very probable that both HgSe(HSe)-

 and HgSe2

-2 species are oligomeric, and this should also 

be taken into account in re-interpreting the data. 
E5 of 7 

E3. Estimated Dissociation Constants of Selenide 

Complexes of As+3, Cd+2, Pb+3, Sb+3, Zn+2 and 

Fe+2 in Aqueous Solution 

In the absence of any information pertaining to selenide complexes for Cd+2, Pb+3, Sb+3, Zn+2 and 

Fe+2, values of their dissociation constants were estimated from those of the corresponding 

sulfide complexes using a linear correlation equation in which were plotted solubility product 

pairs for metal sulfide and selenide minerals, offset values of the dissociation constant pairs for 

H2S(aq) and H2Se(aq), and HS-

 and HSe-, respectively, and dissociation constants for Hg sulfide 

and selenide aqueous complex pairs. The respective sulfide and selenide solubility products are 

calculated from thermochemical, electrochemical or calorimetric data, but not solubility 

measurements. The data used in this plot and the sources of information are given in Table E2. 

Note that metacinnabar and marcasite solubility products are used in place of those for cinnabar 

and pyrite, respectively, in order to compare sulfide and selenide minerals with identical crystal 

structures. 

The initial regression was conducted on all species together, correcting H2Se(aq) and HSespecies values by an identical amount sufficient to bring them into colinearity with the trend of 

the solid phases. The aqueous species were then restored to the original values, and then the 

value for the dissociation constant for HSe-

 adjusted to ensure that the slopes of the two lines 

were identical. This entailed adjustment from Log K(HSe-

) = -14.0 ±1.0 to –14.67, which is well 

within the uncertainty of value assigned by (Seby et al., 2001). The reason for the offset is not 

known, but might be ascribed to the effects of solvation and electrostriction in the aqueous phase 

acting on these molecular and ionic species, e.g., see Shock and Helgeson (1988). The analysis 

and interpretation is quite speculative, and may be modified upon more comprehensive review. 
E6 of 7 

Table E2. Log K Values of Sulfide and Selenide Aqueous Complex and Mineral Pairs used to generate a 

Correlation Plot to Estimate the Dissociation Constants of Unknown Metal Selenide Complexes and 

Effective Solubility Products of Metal Selenides 

Sulfide Selenide 

Species Log K Source Species Log K Source 

Aqueous 

HS- -16.52 1 HSe- -14.67 4 

H2S(aq) -6.99 2 H2Se(aq) -3.81 5 

HgS(aq) -28.08 3 HgSe(aq) -35.43 4 

HgS(HS)- -32.67 3 HgSe(HSe)- -39.10 4 

HgS2

-2 -23.91 3 HgSe2-2 -28.80 4 

Mineral 

CdS (greenockite) -14.09 6 Cadmoselite -18.89 7 

Meta-cinnabar -37.78 6 Tiemannite -43.27 7 

Galena -14.87 6 Clausthalite -20.78 7 

Marcasite -49.87 7 Ferroselite -55.45 7 

Notes: 

1. Dyrssen (1988); 2. Bessinger (2000); 3. Paquette and Helz (1997); 4. this appendix; 5. Seby et al. (2001); 6. 

Appendix D; 7. EQ3/6 Data0.ymp.R4 database (see Appendix D) 

The data summarized in Table E2 and results from the regression analysis are shown in Figure 

E1. The linear regression of the data is good (R2

 = 0.993). The regression equation was used to 

estimate the dissociation constants for the corresponding selenide analogues of sulfide 

complexes of As(III), Cd(II), Pb(II), Sb(III), Zn(II) and Fe(II), which are presented in Table E3. 

These estimated values were incorporated in the thermodynamic databases of EQ3/6 and 

TOUGHREACT, and used to calculate saturation indices of selenide minerals in those NWIS 

groundwaters that were analyzed for Se. 
E7 of 7 

y = 1.1396x - 1 .5358

R2

 = 0.9911

y = 1 .1396x + 4.1557

R2

 = 1

-80

-70

-60

-50

-40

-30

-20

-10

0

-60 -50 -40 -30 -20 -10 0

Log K(sulfide)

Log K(selenide)

Fig. E1. Linear regression of dissociation constants of Hg sulfide and selenide complex pairs, and solubility 

product pairs for metal selenides and sulfides. “aq.” refers to aqueous species. 

Table E3. Estimated Log K Values of Selenide Aqueous Complexes of As+3, Cd+2, Pb+3, Sb+3, Zn+2 and Fe+ 

Reaction Log K 

Arsenic 

H3As3Se6(aq) + 1.5O2(g) + 9H2O = 3H2AsO4

-

 + 6HSe- + 9H+ -23.96 

H2As3Se6

-

 + 1.5O2(g) + 9H2O = 3H2AsO4

-

 + 6HSe- + 8H+ -19.63 

HAs3Se6

-2 + 1.5O2(g) + 9H2O = 3H2AsO4-

 + 6HSe- + 7H+ -10.64 

AsSe(OH)(SeH)- + 0.5O2(g) + 2H2O = H2AsO4

-

 + 2HSe- + 2H+ -6.91 

Cadmium 

CdSe(aq) + H+ = Cd+2 + HSe -10.24 

Cd(HSe)3

-

 = Cd+2 + 3HSe- -23.47 

Cd(HSe)4

-2 = Cd+2 + 4HSe-

 -25.12 

Cd(OH)2(HSe)- + 2H+ = Cd+2 + 3HSe- + 2H2O -1.98 

Lead 

PbSe(aq) + H+ = Pb+2 + HSe- -11.04 

Pb(HSe)2(aq) = Pb+2 + 2HSe- -20.76 

Pb(HSe)3

-

 = Pb+2 + 3HSe- -22.15 

Antimony 

H2Sb2Se4(aq) + 6H2O = 2Sb(OH)3 + 4HSe- + 4H+ -70.63 

HSb2Se4

-

 + 6H2O = 2Sb(OH)3 + 4HSe- + 3H+ -64.88 

Sb2Se4

-2 + 6H2O = 2Sb(OH)3 + 4HSe-

 + 2H+ -54.08 

Zinc 

ZnSe(aq) + H+ = Pb+2 + HSe- -8.76 

Zn(HSe)4

-2 = Zn+2 + 4HSe-

 -21.42 

ZnSe(HSe)- + H+ = Zn+2 + 2HSe- -12.56 

ZnSe(HSe)2

-2 + H+

 = Zn+2 + 3HSe- -11.78 

Iron 

FeSe(aq) + H+ = Fe+2 + HSe- -2.28 
 

 F1 of 16 

 Appendix F: 

Improved Adsorption Models in TOUGHREACT 

F1. Introduction 

Many minerals such as metal oxides, hydroxides and layered silicates (Al2O3, TiO2, FeOOH, 

SiO2, kaolinite, etc.) exhibit electrically charged surfaces in the presence of natural waters. These 

surfaces contain ionizable functional groups (e.g., silanol groups in hydrated silica, Si-OH) being 

responsible for chemical reactions at the surface. Adsorption via surface complexation has been 

widely studied (Dzombak and Morel, 1990; Lutzenkirchen, 2006) and is a key process with 

respect to the fate and transport of heavy metals (e.g., Bradl, 2004) or other hazardous 

constituents such as arsenic (Goldberg, 2002; Goldberg et al., 2005; Manning and Goldberg, 

1997). We have therefore incorporated into TOUGHREACT a model considering adsorption via 

surface complexation, using different surface complexation models such as the non-electrostatic 

model, the double layer model, and the constant capacity model (Dzombak and Morel, 1990). 

The surface complexation models implemented in TOUGHREACT were verified by comparison 

with other codes such as PHREEQC (Parkhurst and Appelo, 1999) and CRUNCH (Steefel, 

2001). This Appendix includes two main parts. In the first part, we described the mathematical 

formulation and numerical implementation of surface complexation adopted in TOUGHREACT, 

while in the second part, we review the surface complexation reactions and their thermodynamic 

data used in the reactive transport simulations conducted in Section 4. 

F2. Surface Complexation Models 

F2.1 Surface Electric Potential Model 

The sorption of solutes on solid surfaces can be described as a chemical reaction between the 

aqueous species and the specific surface sites (surface complexation). These surface reactions 

include proton exchange, cation binding and anion binding via ligand exchange at surface 
 

 F2 of 16 

hydroxil sites (represented here as XOH to avoid confusion with other chemical species). For 

example, the sorption of a metal could be represented as: 

z+ z+-1 + XOH + = + M XOM H (F1) 

At equilibrium, the sorption reactions can be described by the mass law equation: 

[ ]

1

z

z

H

app

M

XOM a

K

XOM a

−

+

+

+

⎡ ⎤ ⎣ ⎦ = (F2) 

where Kapp is referred to as the apparent equilibrium constant, because it includes surface charge 

effects and hence is dependent on the extent of surface ionization (Dzombak and Morel, 1990), 

a is the thermodynamic activity of the aqueous species, and the terms in brackets represent the 

concentration of surface complexes (mol/kg). 

In aqueous complexation reactions, the electric charge is assumed to be homogeneously 

distributed in the solution. However, surface reactions take place on a fixed charged surface 

which creates an electrostatic field. An additional energetic term accounting for the work needed 

for the aqueous species to travel across the surface electric field is required: 

0 ΔΔΔ Δ ΔΔ Δ GGG G GG G ads intr coul intr =0 = intr = + = +( - )= - zF ψ ψ ψ ψ 0 (F3) 

where ΔGads is the free enthalpy change of the overall adsorption reaction, ΔGintr and ΔGcoul are 

the free enthalpy change due to chemical bonding and to the electrostatic work, respectively, z

is the charge of the surface species, F the Faraday's constant (96485 C/mol), and ψ 0 is the mean 

surface potential (V). Since 

ΔG = -RTlnK , (F4) 

Equation (F3) can be rewritten as: 

0 zF

K K app int = e RT

ψ

, (F5) 

where R is the gas constant (8.314 J/mol/K), T is the absolute temperature (K), and Kint is the 

intrinsic equilibrium constant which does not depend on the surface charge. 
 

 F3 of 16 

F2.2 Diffuse Layer Model 

The diffuse layer model has been described in great detail by Dzombak and Morel (1990) and 

was applied to adsorption of metals on iron oxide surfaces. In the diffuse layer model, the solidwater interface is composed of two layers: a layer of surface-bound complexes and a diffuse 

layer of counter ions in solution. The surface charge is calculated from the total surface species 

adsorbed on the layer: 

Ns

p k k

k=1

F

= z y A σ ∑ (F6) 

According to the Gouy-Chapman theory, the surface charge density σ p (c/m2

) is related to the 

potential at the surface (volt) by: 

3

0 10 sinh 1/2

d 0 σ ε = (8RT I (zF /2RT) ε × ) ψ , (F7) 

where R is the molar gas constant (8.314 J mol-1 K-1), T is the absolute temperature (K), ε is the 

relative dielectric constant of water (ε = 78.5 at 25 oC), 0 ε is the permittivity of free space 

(8.854×10-12 C V-1 m

-1 or 8.854×10-12 C2

 J-1 m

-1), and c is the molar electrolyte concentration 

(M). Equation (F7) is only valid for a symmetrical electrolyte (Z=ionic charge). 

It is common to use the linearized version of Equation (F7) for low values of the potential: 

σ p 0 0 = εε κψ , (F8) 

where 1/κ (m) is the double-layer thickness defined as: 

1/2

1000

0

2

1 RT = ( ) 2 I Fε ε

κ ⋅ (F9) 

Equations (F7) can be simplified by rewritten 3 10 1/2

0 (8RT I ε ε × ) for 25 o

C : 

1/2

p 0.1174 sinh d σ = I (zF /2RT) ψ (F10) 
 

 F4 of 16 

Therefore, in the diffuse-layer model, the value of the capacitance C relating the surface charge 

and the potential can be calculated based on theoretical considerations (Equation (F8)) instead of 

being an experimental fitting parameter. 

F2.3 Constant Capacitance Model 

Similar to the diffused-layer, the constant capacitance model is based on the assumption that all 

the species are adsorbed in the same layer and a diffuse layer of counterions constitutes the 

transition to homogenous solution. However, differently from the diffuse-layer model, the 

relationship between the surface charge and the potential is assumed to be linear: 

σ = Cψ (F11) 

where C is a constant value to be obtained from fitting experimental data. 

F2.4 Mathematical Formulation of Adsorption Reactions 

Lets us consider the following surface desorption reaction: 

XOM = XOH + M - H z+-1 z+ +

with the equilibrium constant of the reaction given as: 

[ ]

[ ]

0

int

zF

M RT

H

XOH a

K e

XOM a

ψ

+

⎛ ⎞ −

⎜ ⎟ ⎝ ⎠ = (F12) 

Usually, the neutral surface complex XOH is included in the NC aqueous primary species. The 

new unknown is the potential term 

F 0

RT

ψ ψ −  = (F13) 

The concentration of a surface complex, yj, (mol/m3

), can be expressed in terms of the 

concentration of the primary species and the potential term: 
 

 F5 of 16 

1 C y y ji ji j

N z -1

j j i i Y

i=1

y = e K c N j= ... ν ν ψ

∏ γ 

 (F14) 

where Kj is the intrinsic equilibrium constant of the desorption reaction. 

In order to solve for the potential term, an additional equilibrium equation is needed. For the 

double diffuse layer model, we obtain Equation (F15), if the Guy-Chapman double-layer theory 

is used to relate surface charge and the potential (Equation (F7)). If the linearized Equation (F8) 

is used, we obtain Equation (F16). If a constant capacity model is used, we obtain Equation 

(F17). 

3 10 sinh 0

Ns

1/2

0 k k

k=1

F (8RT I ( /2) z ) y A ε ψ ε × −− =  ∑ (F15) 

2

0

0

Ns

k k

k=1

F

 z y A RT ψ

εε κ ∑ +  = (F16) 

2

0

Ns

k k

k=1

F

 z y ACRT ∑ +ψ = (F17) 

F2.5 Implementation in TOUGHREACT 

When surface complexation is considered, the total concentration of the primary species j at time 

zero (initial conditions) is assumed to be known, given by: 

00 0 0 0 0 

Nx s Np q N N

j j kj k mj m nj n sj s C

k=1 m=1 n=1 s=1

T = c c c c c j = 1... ++ ++ ∑∑ ∑ ∑ νν ν ν N (F18) 

where superscript 0 represents time zero; c denotes concentration; subscripts j, k, m, n and s are 

the indices of the primary species, aqueous complexes, minerals at equilibrium, minerals under 

kinetic constraints, and surface complexes, respectively; Nc, Nx, Np, Nq and Ns are the number of 

corresponding species and minerals; and νkj, νmj, νnj and νsj are stoichiometric coefficients of the 

primary species in the aqueous complexes, equilibrium, kinetic minerals, and surface complexes, 

respectively. 
 

 F6 of 16 

After a time step Δt , the total concentration of primary species j (Tj ) is given by 

( ) 0 x p q s N N N N

j j kj k mj m nj n n sj s C

k=1 m=1 n=1 s=1

T = c c c c r t c j = 1... + + + −Δ+ ∑∑ ∑ ∑ νν ν ν N (F19) 

where rn is the kinetic rate of mineral dissolution (negative for precipitation, units used here are 

mole per kilogram of water per time), for which a general multi-mechanism rate law can be used 

(Xu et al., 2006). 0 Tj and Tj are related through generation of j among primary species as follows 

0

C j 1,...N

N a

j j lj l

l=1

T T r t −= Δ = ∑ν (F20) 

where l is the aqueous kinetic reaction (including biodegradation) index, Na is total number of 

kinetic reactions among primary species, and rl is the kinetic rate which is in terms of one mole 

of product species. 

By substituting Equations (F18) and (F19) into Equation (F20), and denoting residuals as c Fj

(which are zero in the limit of convergence), we have 

0

0

0

 ( ) Primary species 

 ( ) equilibrium aqueous complexes 

 ( ) equilibrium

x

p

c

j j j

N

kj k k

k=1

N

mj m m

m=1

F = c c 

 cc

 cc

ν

ν

−

+ −

+ −

∑

∑

1

minerals 

 kinetic minerals 

 kinetics among primary species 

 

q

a

N

nj n

n=1

N

lj l

l

 rt

 rt

ν

ν

=

− Δ

− Δ

+

∑

∑

0 ( ) surface complexes 

 0 

Ns

sj s s

s=1

C

 cc

 j = 1...N

ν −

=

∑

 (F21) 

According to mass-action equations, the concentrations of aqueous complexes ck can be 

expressed as functions of concentrations of the primary species cj. Kinetic rates rn and rl are 

functions of cj. The expression for rn is given in Xu et al. (2006), and rl will be presented later. 
 

 F7 of 16 

No explicit expressions relate equilibrium mineral concentrations cm to cj. Therefore, NP

additional mass-action equations (one per mineral) are needed. Surface complexes are expressed as 

the product of primary species and an additional potential term ψ . Additional equilibrium 

equations (Equation (F16) or (F17) depending on the surface complexation model) have to be 

solved together with Equation (F21) and the Np mass-action equations for equilibrium minerals. 

The final Jacobian matrix has Nc+Np+ Nψ rows and Nc+Np+ Nψ columns, as follows: 

ccc

jjj

i i

ppp

jjj

i i

i i

Nc Np N

FFF

Nc

c p

FFF

Np

c p

FFF N

c p

ψ ψ ψ

ψ

ψ

ψ

ψ

ψ

∂ ∂ ∂

∂ ∂ ∂

∂ ∂ ∂

∂ ∂ ∂

∂ ∂ ∂

∂ ∂ ∂









 (F22) 

x c N Nq Ns

cc j k nn n s

ji kj ik nj in sj is

i i ii k=1 n=1 s=1

F c kA c J 

c c cc

δ νν νν νν

∂ Ω

= =+ + +

∂ ∑∑ ∑ (F23) 

c Ns

c j

sjss

s=1

F

J zc ψ ν

ψ

∂ = = ∂ ∑ 

 (F24) 

Ns

c s

s is

i i s=1

F c J z

c c

ψ

ψ ν

∂ = =− ∂ ∑ 

 (F25) 

or 

2

0

Ns

c s

s is

i i s=1

F F c J z

c A RT c

ψ

ψ ν

εε κ

∂ = = ∂ ∑ 

 (F26) 

or 

2 Ns

c s

s is

i i s=1

F F c J z

c ACRT c

ψ

ψ ν

∂ = = ∂ ∑ 

 (F27) 

1 3

2 10 cosh

Ns

1/2

0 k k k

k=1

F F J (8RT I ( /2) z z y ) A

ψ

ψψ ε ψ ε

ψ

∂

= =− × − −

∂ ∑ 

    (F28) 

or 
 

 F8 of 16 

2

0

1

Ns

sss

s=1

F F J zzc

A RT

ψ

ψψ

ψ εε κ

∂

== +

∂ ∑ 

 

 (F29) 

or 

2

1

Ns

sss

s=1

F F J zzc

ACRT

ψ

ψψ

ψ

∂

== +

∂ ∑ 

 

 (F30) 

F2.6 Verification in Comparison with Other Codes 

The surface complexation calculation by TOUGHREACT has been verified against other codes 

such as PHREEQC (Parkhurst and Appelo, 1999) and CRUNCH (Steefel, 2001). The example of 

zinc sorption at low site density presented by Dzombak and Morel (1990) has been used by 

PHREEQC and CRUNCH to verify their surface complexation calculation. Zinc sorption on 

hydrous ferric oxide is simulated assuming two types of sites, weak and strong. Protons and zinc 

ions compete for the two types of binding sites, and equilibrium is described by mass-action 

equations. The double layer model is used to calculate the speciation of Zn2+ in aqueous and 

surface complexes. The example considers the variation in sorption of zinc on hydrous ferric 

oxides as a function of pH for low zinc concentration (10-7 M) and high zinc concentrations (10-4

M) in 0.1 M sodium nitrate electrolyte. 

Figure F1 shows the calculation results of Dzombak and Morel (1990). Table 1 compares the 

calculated equilibrium speciation of Zn2+ calculated by TOUGHREACT and CRUNCH. Both 

simulators arrive at almost identical results. Figures F2 shows the calculated equilibrium 

speciation of Zn2+ as a function of pH calculated by TOUGHREACT. 
 

 F9 of 16 

Fig. F1. Calculated equilibrium speciation as a function of pH for zinc in a 90-mg/L HFO suspension: The top 

figure shows results for Total Zn=10-7 M, I = 0.1 M; the bottom figure shows results for Total Zn = 10-

4

 M, I = 0.1 M (Dzombak and Morel, 1990). 
 

 F10 of 16 

Table F1. Calculated equilibrium speciation for zinc by TOUGHREACT and by CRUNCH 

pH Zn2+ Fes

OZn+ FewOZn+

 TOUGHREACT CRUNCH TOUGHREACT CRUNCH TOUGHREACT CRUNCH 

5 9.94E-08 9.94E-08 5.51E-10 6.03E-10 2.31E-11 2.52E-11 

6 6.48E-08 6.28E-08 3.38E-08 3.57E-08 1.43E-09 1.51E-09 

7 2.60E-09 2.61E-09 9.34E-08 9.34E-08 3.99E-09 3.99E-09 

8.062 1.40E-10 1.38E-10 9.58E-08 9.58E-08 4.09E-09 4.09E-09 

6

7

8

9

10

11

456789

pH

-log C

Zn2+

3

4

5

6

7

8

456789

pH

-log C

Zn2+

FesoZn+

FewoZn+

Fig. F2. Calculated equilibrium speciation as a function of pH for zinc. The top figure shows results for Total 

Zn=10-4 M, I = 0.1 M; the bottom figure shows results for Total Zn=10-7 M, I = 0.1 M. 
 

 F11 of 16 

F3. Surface Complexation Reactions and 

Parameters Used in Section 4 

The adsorption of heavy metal (and arsenic) ions on minerals is influenced by a variety of 

factors, the most important being pH, type and speciation of metal ion involved, as well as heavy 

metal competition (Bradl, 2004). In this study, we consider goethite, illite, kaolinite, and smectite 

as principal adsorbents, mainly because these minerals have strong adsorption capacity, and we 

focus on lead and arsenic as relevant hazardous constituents. A literature analysis was conducted 

to determine the relevant surface complexation reactions and their thermodynamic constants for 

these cations and minerals, as summarized below. 

Adsorption of Pb(II) on goethite was investigated under varying conditions. Kovacevic et al. 

(2000) experimentally studied the adsorption of lead species on goethite. Experiments were 

carried out for total Pb species concentrations of 2.5 × 10-4 mol/L, ph of 3–6, and ionic strength 

of 2 × 10-3 mol/L. The calculated surface complexation constant (logK) is -0.9 for a reaction 

expressed in the following form: 

2 SOPb H Pb SOH ++ + + + U (F31) 

where S indicates surface, and SOPb+ and SOH are surface complexes. 

Muller and Sigg (1991) investigated the adsorption of Pb(II) on goethite in dilute (i.e., 2.41 × 10-

7

 mol/L) Pb (II) solutions over a pH range 6.6–8.2. Their published surface complexation 

constant (logK) is 0.52 for the monodentate surface complex (Equation F31) and 6.27 for the 

bidentate surface complex. Published surface complexation constants by Muller and Sigg (1991) 

are adopted in our simulations here, since they were obtained under conditions similar to the 

simulation setup in terms of Pb(II) concentration and pH. Since PbCO3(aq) is used as the 

primary species in our simulation instead of Pb(II), the reaction described in Equation (F31) 

occurs together with the following reaction between PbCO3(aq) and Pb(II): 

2

3 3 Pb HCO H PbCO aq K ( ) log 3.06 + −+ + + =− U (F32) 

Note that the logK value of Equation (F32) was taken from the EQ3/6 V7.2b data base (Wolery, 

1993). 
 

 F12 of 16 

Combining Equations (F31) and (F32), we obtain the surface complexation reaction shown in 

Table F2, where logK is -2.56. Note that the surface complex is expressed as _ Goe OPb+ instead 

of SOPb+

, in order to distinguish it from other surface complexes of Pb(II). 

Hizal and Apak (2006) studied lead(II) adsorption individually on kaolinite-based clay minerals. 

They performed experiments on three samples and calculated surface complexation constants 

and site densities. In our study, the surface complexation constant is taken to be the average of 

the value for these three samples, which is -1.87 for Equation (F31). Combining Equations (F31) 

and (F32), the logK value is -4.95. 

Using batch adsorption experiments, Gu and Evans (2007) investigated the adsorption of Pb(II) 

onto illite as a function of pH and ionic strength. (Surface complexation of Pb on illite in a 0.01 

mol/L NaNO3 electrolyte is considered in our model because the majority of the potable 

groundwaters recorded in the NWIS database (USGS, 2008) have an ionic strength around 

0.01M.) The investigators provided surface complexation constants for complexes on both edge 

and basal sites. Here, we refer to these as constants for strong sites (Ills

_OPb+) and weak sites 

(Illw_OPb+

), respectively (see Table F2). 

Bradbury and Baeyens (2005) summarized surface complexation constants on smectite for more 

than ten metals. They presented linear free energy relationships (LFER) for strong and weak 

sites. The LFER correlated the surface complexation constants with aqueous complexation 

constants of the corresponding aqueous complexes. The calculated surface complexation 

constants for Pb(II) on montmorillonite, a member of the smectite group, for strong and weak 

sites using the LFERs (see Table 6 in Bradbury and Baeyens, (2005) are adopted in our 

simulation (see Table F2). 

All the surface complexation reactions for lead used in the present simulation are listed in Table 

F2, while Table F3 summarizes these reactions using Pb+2 as primary species. 

Adsorption of arsenite species on minerals can be described by three surface complexation 

reactions: 

232 33 SH AsO H O SOH H AsO + U + (F33) 
 

 F13 of 16 

3 2 33 SHAsO H O H SOH H AsO − + ++ + U (F34) 

2

3 2 33 SHAsO H O H SOH H AsO 2 − + ++ + U (F35) 

where S denotes the surface of any mineral. 

Surface complexation constants (see Table F3) for adsorption of arsenite on goethite are taken 

from Dixit and Hering (2003) by combining two reactions: 

3

232 3 Goe OH AsO H O Goe OH AsO H K _ _ 3 log 39.93 − + + + + =− U (F36) 

3

3 33 AsO H H AsO K 3 log 34.74 − + + = U (F37) 

Goldberg (2002) published the surface complexation constants (logK) for Equations (F36) and 

(37), which are -4.68 and 2.27, respectively. These constants are similar to those reported by 

Dixit and Hering (2003). 

Goldberg (2002) also presented the surface complexation constants for the adsorption of arsenite 

on illite, kaolinite, and smectite. However, Manning and Goldberg (1997) gave a more complete 

description for adsorption of arsenite on these three minerals. In our study, we use the published 

surface complexation constants for Equations (F33), (F34) and (F35) from Manning and 

Goldberg (1997) (see Table F4). Table F4 summarizes the surface complexation reactions of As 

used in Section 4. 

Specific surface area and site density are the other two important parameters affecting 

adsorption. Table F5 summarizes some published values for specific surface area and site 

density, while Table F6 lists those values used in the base model. 
 

 F14 of 16 

Table F2. Surface complexation of Pb on different minerals using PbCO3(aq) as primary species 

Adsorbent Surface 

Complexes 

Reactions Log 

kint

Reference 

goethite Goe_OPb+ Goe _OPb+

 + HCO3

- U Goe_OH + 

PbCO3(aq) 

-2.56 

goethite (Goe _O)2Pb (Goe _O)2Pb + H+

 + HCO3

- U 2Goe_OH 

+ PbCO3(aq) 

3.18 

(Muller and Sigg, 1991) 

kaolinite Kao_OPb+ Kao_OPb+

 + HCO3- U Kao_OH + 

PbCO3(aq) 

-4.95 (Hizal and Apak, 2006) 

illite Ills_OPb+ Ills

_OPb+ + HCO3- U Ills_OH + 

PbCO3(aq) 

-4.43 

illite Illw_OPb+ Illw_OPb+

 + HCO3- U Illw_OH + 

PbCO3(aq) 

0.78 

(Gu and Evans, 2007) 

smectite Smes_OPb+ Smes

_OPb+ + HCO3- U Smes_OH + 

PbCO3(aq) 

-4.18 

smectite Smew_OPb+ Smew_OPb+

 + HCO3- U Smew_OH + 

PbCO3(aq) 

-1.78 

(Bradbury and Baeyens, 

2005) 

glauconite Gla_OPb+ Gla_OPb+

 + HCO3

- U Gla _OH + 

PbCO3(aq) 

-6.4 (Smith et al., 1996) 

Table F3. Surface complexation of Pb on different minerals using Pb+2 as primary species. 

Adsorbent Surface 

Complexes 

Reactions Log 

kint

Reference 

goethite Goe_OPb+ Goe _OPb+

 + H+ U Goe_OH + Pb+2 0.5 

goethite (Goe _O)2Pb (Goe _O)2Pb+2H+U 2Goe_OH + Pb+2 6.24 

(Muller and Sigg, 1991) 

kaolinite Kao_OPb+ Kao_OPb+

 + H+ U Kao_OH + Pb+2 -1.89 (Hizal and Apak, 2006) 

illite Ills_OPb+ Ills

_OPb+ + H+U Ills_OH + Pb+2 -1.37 

illite Illw_OPb+ Illw_OPb+

 + H+ U Illw_OH + Pb+2 3.84 

(Gu and Evans, 2007) 

smectite Smes_OPb+ Smes

_OPb+ + H+ U Smes_OH + Pb+2 -1.12 

smectite Smew_OPb+ Smew_OPb+

 + H+ U Smew_OH + Pb+2 1.28 

(Bradbury and Baeyens, 

2005) 

glauconite Gla_OPb+ Gla_OPb+

 + H+ U Gla _OH + Pb+2 -3.34 (Smith et al., 1996) 
 

 F15 of 16 

Table F4. Surface complexation of H3AsO3 on different minerals 

Adsorbent Surface 

Complexes 

Reactions Log kint Reference 

goethite Goe _H2AsO3 Goe _H2AsO3 + H2O U Goe _OH + H3AsO3 -5.19 

goethite Goe _HAsO3

- Goe _HAsO3

-

 + H2O + H+U Goe_OH + 

H3AsO3

2.34 

(Dixit and 

Hering, 2003) 

kaolinite Kao_H2AsO3 Kao _H2AsO3 + H2O U Kao _OH + H3AsO3 -8.23 

kaolinite Kao _HAsO3

- Kao_HAsO3

-

 + H2O + H+U Kao _OH + 

H3AsO3

0.664 

kaolinite Kao _OAsO3

2- Kao_AsO3

2- + H2O + 2H+U Kao _OH + 

H3AsO3

13.67 

(Manning and 

Goldberg, 

1997) 

illite Ill_H2AsO3 Ill _H2AsO3 + H2O U Ill_OH + H3AsO3 -9.07 

illite Ill _HAsO3

- Ill _HAsO3

-

 + H2O + H+U Ill _OH + H3AsO3 -3.000 

illite Ill_AsO3

2- Ill_AsO3

2- + H2O + 2H+U Ill_OH + H3AsO3 10.3 

(Manning and 

Goldberg, 

1997) 

smectite Sme _H2AsO3 Sme _H2AsO3 + H2O U Sme _OH + H3AsO3 -8.89 

smectite Sme _HAsO3

- Sme _HAsO3

-

 + H2O + H+U Sme _OH + 

H3AsO3

 4.650 

smectite Sme _AsO3

2- Sme _AsO3

2- + H2O + 2H+U Sme _OH + 

H3AsO3

13.700 

(Manning and 

Goldberg, 

1997) 

albite~low Alb _OH2AsO3 Alb _H2AsO3 + H2O U Alb _OH + H3AsO3 -7.58 

albite~low Alb _OHAsO3

- Alb _OHAsO3

-

 + H2O + H+U Alb _OH + 

H3AsO3

2.43 

albite~low Alb _OAsO3

2- Alb _OAsO3

2- + H2O + 2H+U Alb _OH + 

H3AsO3

14.8 

(Manning and 

Goldberg, 

1997) 

Table F5. Summary of published specific surface area and site densities 

Site density (mol/m2

) Absorbent Specific surface area 

(m2

/g) Strong site Weak site Reference 

hematite 5.4 0.00219 0.219 Dzombak and morel (1990) 

goethite 54 3.32e-6 Dixit and Hering (2003) 

goethite 14.7 1.76e-6 3.22e-6 Muller and Sigg (1991) 

kaolinite 19.53 1.02 e-8 Hizal and Apak (2006) 

kaolinite 14.73 2.2e-6 3.0e-6 Lackovic, et al. (2003) 

kaolinite 9.1 2.68e-7 Manning and Goldberg (1997)

illite 66.8 1.3e-6 2.27e-6 Gu and Evans (2007) 

illite 123 2e-7 6e-7 Lackovic, et al. (2003) 

illite 24.2 4.79e-8 Manning and Goldberg (1997)

Na-smectite 56.38 4.77e-8 9.54e-7 Bradbury and Baeyens (2005) 

smectite 18.6 1.1e-6 Manning and Goldberg (1997) 

albite~low 5.9 2.9e-5 Manning and Goldberg (1997) 

glauconite 48.7 1.6e-6 Smith et al. (1996) 
 

 F16 of 16 

Table F6. Specific surface area and site densities used in the base model 

Site density (mol/m2

) Absorbent Specific surface area 

(m2

/g) Strong site Weak site Reference 

goethite 14.7 1.76e-6 3.22e-6 Muller and Sigg, (1991) 

kaolinite 14.73 2.2e-6 3.0e-6 Lackovic et al. (2003) 

illite 66.8 1.3e-6 2.27e-6 Gu and Evans (2007) 

Na-smectite 56.38 4.77e-8 9.54e-7 Bradbury and Baeyens (2005)